The task force will now come to order. Without objection, the chairs authorized to declare a recess at any time without objection members of the full committee are authorized to participate in todays hearing consistent with the committees practice. This hearing is entitled robots on wall street, the impact of ai on jobs in the Financial Services industry. The chair will recognize himself for five minutes for an opening statement. Thank you all for joining us today for what should be a having interesting hearing of this task force. Were looking at exploring how ai is being deployed in Capital Markets from automated trading to Investment Management decisions. Were going to consider how the use of this technology is changing the nature of work in Financial Services, rendering some jobs obsolete and changing the skill sets needed to excel in others. It would not be much of an exaggeration today to say that wall street is run by computers. I actually hear about those days from the limo driver who takes me back who used to be a floor trader on the merck. Today trades are automated and executed in microseconds. Etfs rely on algorithm models. Quantitative hedge funds use algorithms that look for correlations that will in the market to provide the most value for investors. And i think its very notable that a lot of the shakeout that were seeing in those markets is really a reflection of sort of the winner take all nature of digital economies. That any Digital Business is a natural monopoly and youre going to see more and more of the rewards go to a smaller and smaller number of dominant players. I would like to emphasize that doesnt mean theyre eventually. Its a natural reflection of the nature of the digital marketplace. Other Asset Managers may use algorithms to perform research and analysis in realtime on big data sets. It can include scouring of social media sites, online tractio transactions and this is i guess good in terms of having the market reflect all known data but there are abuse of corners. Imagine what it would be worth if you had a 10second early look at trumps twitter feed, how much you could make trading off that, for example. Three types of computer managed funds make up about 35 of the approximate 31 trillion public equities markets. Hedge funds manage just 24 of the market. The rise of computerization of our stock markets has a number of benefits. The cost of executing trades has gone way down and theres more liquidity in the market. Passive funds charge less than 1 while active managers charge 20 times that much. It creates additional questions. As in the flash crash ahave shon trading can create consequences that create market volatility and create exacerbation between investors as firms with more and faster access to enormous data sets are able to obtain a competitive advantage. Another broader question is how these developments are impacting the nature of jobs in the Financial Services industry. Recent Wells Fargo Research report estimated that efficiencies would result in about 200,000 job cuts in the u. S. Banking industry. While these cuts will affect back office, call center and Customer Service positions, the pain will be widespread. Many Front Office Workers could also see their head count drop by almost a third according to a report released earlier this year. The report also found that 40 of existing jobs at Financial Firms could be automated with current technology. To first order if you spend your day staring at a big screen and if youre receiving a large paycheck, your job will be at risk. Understanding the skills that will be needed to excel in the Financial Services industry of tomorrow and how we can encourage these skills is one of the issues that we must tackle head on and tackle early. In a world where many functions can be done by automated ai models, what role does that leave for humans . I look forward to hearing from our witnesses on these issues. I would like to recognize my friend from georgia for five minutes. Thank you, mr. Chairman. And i want to thank each of our witnesses here today. Thank you for taking time to be here to discuss this issue while the rest of america is fixated on other things going on here, this is something that may not resonate on the major americas but it is something thats very important, has an impact on our lives positively but also potentially negatively and its important that we be looking into this. And as you know, today the task force will examine the intersection between technology and the Capital Markets. In recent years there have been many developments including the adoption of Artificial Intelligence and automation that have redefined and reshaped trading and investing. The first trades on the new York City Stock Exchange were made in the late 1700s using a manual paper process. They communicated about orders over the phone and today its done on digital platforms. Electronic trading has benefitted the market in many ways, lowering overhead and transaction cost. Several major Asset Management firms now offer 0 commissions which means investors can sell stocks for free and can capture more of the growth of their investments. This would not be possible without electronic trading. Digital trading platforms provide investors with access to low cost advice 24 hours a day using robo advisers. It makes markets more efficient, better processing of large sets of data and more transparent price information. The proliferation of technology can lower firms barriers to entry, improve Risk Management and increase Market Access for investors. In addition do these benefits, there are many other cases of Companies Using ai to improve efficiencies in the Capital Markets in unique ways. For example, some Clearing Companies are using ai to optimize the settlement of trades. Some selfregulatory organizations are using ai in market surveillance. While there are many benefits to electronic trading it can present new challenge. One challenge which is at the forefront of our discussion today is the disruption of job market. While the rise of automated trading has displaced many floor traders, Job Opportunities in fields like code writing, cloud management, telecommunications, fiberoptics and Data Analysis are growing. Theres some concern that High Frequency trading can contribute to volatility, but new evidence suggests that High Frequency trading does not increase volatility and can actually improve liquidity. Theres some concern that firms dont have the latest Technology Firms that dont have the latest technology could be competed out of the markets. Its important to keep in mind that not all types of electronic trading are the same and i look forward to learning more from the witnesses about the differences between trading. Finally i look forward to exploring the issues in this space. One issue i think needs to be addressed is the protection of source code because algorithms are traders core intellectual property, they must be protected. We passed out a pill bill out os Committee Last congress to ensure that the Security ExchangeCommission Issues a subpoena before obtaining these algorithms rather than getting them through routine examines. I hope that well be able to Work Together on a bill this congress and i thank you and i yield back. Thank you. And today were welcoming the testimony of the professor of media culture and communication at nyu. Professor of practice engineering school, cornell university, and chief Investment Officer or true positive technologies. Senior director, future of finance. Chief executive officer modern markets initiative, head of Nasdaq Market surveillance, nasdaq stock market. Witnesses are reminded that your oral testimony will be limited to five minutes and without objection your full written statement will be made part of the record. Youre now recognized for five minutes to give an oral presentation of your testimony. Chairman foster and Ranking Member, thank you for inviting me to testimony. While my written remarks cover four key areas, my oral remarks cover two. We have ample reason to be concerned about automations future in the Financial Services sector. First, the Financial Services sector is ripe for automation and innovation. Second, the fintech sector is on the rise. A large number of workers will be displaced even the Ai Development is projected to create new types of jobs. If all of this is true, then the cause for concern is clear, it lies with the fact that africanamericans are already vastly underrepresented in the Financial Service sector workforce. Africanamericans, hispanics and asians make up only 22 of the Financial Service industry workforce. Africanamerican reputation in the Financial Services sector at both entry level and senior level jobs declined from 2007 to 2015. Less than 3. 5 of all Financial Planners in the u. S. Are black or latino. Africanamericans make up 4. 4 and hispanics just 2. 9 of the securities subsectors. Asians make 2. 8 of the central banking and insurance subsectors. Racial groups that are extremely underrepresented in the Financial Services industry will be most at risk in terms of automation and the escalation of fintech development. This is especially true given the vast underrepresentation of africanamericans in the workforce. If we are to mitigate the likelihood that it will negatively affect those already underrepresented in the Financial Services industry, we must plan ahead, long into the future, rather than allowing the market to run its course towards predictable outcomes. Now to the subject of deterring algorithm bias. And providing more oversight from industry and government who are able to assess the systems that are used. This includes tech collusions that make algorithms more transparent and mitigate against potential biases before such systems gain widespread use rather than trying to correct their affects once their damage is done. When it comes to mitigating the potential outcomes that biassed algorithms might have on individuals and communities of color, simple reliance on technical fixes by technologists is not a complete solution. I want to end by drawling on the wisdom of a former civil rights leader who had a sophisticated understanding of commuterized automation as they existed in his time. He said, today the unskilled worker is the victim, but cyber nation invades the strongholds of the american middle class as workers begin sinking into the alienated world of the american underclass. We find out that automation is a curse. But it is not the only curse. The chief problem is not automation but social injustice itself. Take as a final example the findings of a recent study titled Consumer Lending discrimination in the fintech era. There researchers sought to determine whether a system could reduce discrimination in mortgage lending as compared to traditional face to face systems. It meant that the process that the process still discriminated against a large number of loan applicants. Even though it did not disdiscriminate in terms of low approval. It discriminated against black and latino user. It states that both fintech and face to face lenders may discriminate through pricing strategies. Were just scratching the surface in this area. Lending may reduce discrimination relative to face to face lenders but algorithm lending does not guileliminate. Thank you for allowing me the opportunity to contribute to these proceedings. Thank you. And dr. Lopez, youre recognized for five minutes to give an oral presentation of your testimony. Thank you. Its an honor to be asked to contribute to this today. As a result of recent advances in supercomputing and big data, algorithms can performance tasks that only expert humans could accomplish. An area of particular area of interest is investments. Some of the most successful hedge funds in history are algorithmic. This advantage is that they are able to enable cost reductions. Automated tasks include construction, forecasting, and detection. It creates a number of challenges for the over 6 Million People financed in the industry. Many of whom will lose their jobs not because they will be replaced by machines but because they have not been trained to work along side algorithms. The restraining of these workers is a difficult task but not everything is bad news. As Technical Skills become more important, the wage gap between genders, ethnicities should narrow. Restraining our existing workforce is of critical importance, however, it is not enough. We must make sure that the talent that american universities have contributed and develop remains in our country. The founders of the next school amazon or apple are attending a math or engineering class and like in the past, theyre in our country on a student visa and they will have a very hard time remaining in the United States unless we help them. And as we help them, they will return to their countries of origin where they will compete against us. On a different note, i would like to draw your attention to two practical examples of machinelearning regular laces. Its crowd sourcing of investigations. One of the tasks is to identify market manipulators. Its a challenging task, like searching for a needle in the haystack. The practical approach is for regulators to enroll the data science community. Accordingly, regulators could offer it to offered to data sci who could be rewarded with a portion of the fines levied. The next time the Financial Markets experienced Something Like this, it could leave to identification to potential market manipulators. The second embodiment is false products. Its Financial Firms offer online tools and even large hedge funds fall constantly for this trap leading to investor losses. With this information, all regulators could compete the probability that its overfit and the probability could be promoted in the material. Finally, i would like to conclude my remarks with a discussion. Yes, Machine Learning, we have a better chance of detecting the bias of algorithms. The reason is that we can subject algorithms to randomized controlled experiments and perform as intended. Algorithms can assist human Decision Makers that humans can override. Thus exposing biases in humans. Congress and regulators can play from the mental role in helping the reap the benefits of this technology while mitigating the risk. Thank you for the opportunity to contribute to this hearing. I look forward to your questions. Thank you. Ms. Fender, youre now recognized for five minutes to give an oral presentation of your testimony. Chairman foster, Ranking Member loudermilk and members of the task force. Thank you for inviting me to testify here today. My name is rebecca fender and the senior director of future of finance, our thought leadership program. Its the largest Nonprofit Association of investment professionals in the world. With 170,000 cfa charter holders in 76 countries. Were best known for the cfa charter, which is a rigorous three part graduate level exam. To earn the designation, charter holders must have four years of industry experience. Were a Nonpartisan Organization and seeks to be a leading voice to the global issues of transparency, market efficiency and Investor Protection. Earlier this year, the Institute Published a paper, examining the changing roles and skills of the industry in the next five to ten years. Among the members and candidates we surveyed. 43 think the role they perform today will be substantially different in five to ten years time. It was greater than 50 among financial advisors, traders and risk analysts. 5 do not think their role will exist by then. One of the catalysts is technology. Cfa institute sees the impact of technology on jobs as a pyramid. At the foundation we have basic applications. Everyone will need to learn to do things differently. They must be more comfortable using and understanding technology. Some people will face tech substitution, but many more will have their roles adapted. In the middle there are specialist applications where technology will enhance work. At the top there are hyperspecialist roles that will be less common but valuable. This includes roles in a. I. Labs. The key to this evolution is ongoing learning. Among the members and candidates we surveyed in our recent report, 58 have interest in Data Analysis coding languages, like python. Similarly Data Visualization is areas more than half have expressed interest in. In terms of Artificial Intelligence, the organizing principle we see is Artificial Intelligence plus human intelligence or a. I. Plus h. I. In these middle and top levels about technology hierarchy, Investment Management and Technology TeamsWork Together. A. I. Techniques can augment human intelligence to free investment professionals from routine tasks and enable smarter Decision Making. Investment professionals will spend less time finding and entering data and more time insuring models are consistent with more markets work. A. I. Unlocks the potential of unstructured data and can identify patterns and information more efficiently than humans. A. I. Can amplify an investment teams performance, but cannot replicate its creative. In a recent paper a. I. Pioneers and finance management, we have identified three types of a. I. And big data applications that are emerging in Investment Management. They are, first, the use of natural language processing, Computer Vision and Voice Recognition to efficiently process text, image and audio data. Second, the use of Machine Learning techniques to improve the effectiveness of algorithms. Third, the use of a. I. Techniques to process big data, including alternative and unstructured data for investment insights. We find that few investment professionals, about 10 are currently using a. I. For Machine Learning techniques in their investment processes. Here are a few examples from our case studies of what the a. I. Pioneers are doing. First, Goldman SachsResearch Team is better able to analyze concrete companies by using geo spatial data of u. S. Quarries. Psychology textbooks were studied to determine where spin, omission and blame are being used. Finally, bloomberg has had a Sentiment Analysis product since 2009, which analyzes the potential effect of news stories on valuations. They put 2000 documents a day through their platform. This was uniform data used by hedge funds at first, but now many of their clients use it. Regulators can look at new data in the world of tech. The speed and vauseument of data presents a new challenge. Regulators will need to have tools and resources to keep pace with changes. Thank you again for the opportunity to testify today and i look forward to your questions. Thank you. Youre now recognized for five minutes to give an oral presentation of your testimony. Thank you, chair foster and Ranking Member loudermilk and members of the a. I. Task force. Its an honor to discuss the role of automation of the markets and our deployment of Artificial Intelligence in the Financial Services injury and our future of workforce. Im the chief executive officer of modern markets initiative. Were an education and advocacy organization. Were operating in 50 markets globally and together employ over 1,600 people. Our board, which is have women promotes responsible innovation, including advancing a diverse work force in our industry. Over the past decades, weve seen automated trading leading to much of the replacement at the Exchange Floor based intermediaries, you see a 1980s wall street movies. Technology as you noted has reduced the cost of trading for the average investor by more than half over the past decade. Both in direct trading costs and savings through tighting spreads. So if youre an investor in a 529 College Saving plan, a pension fund, or a 401 k , then you benefitted from todays low cost trading. And all the dependable luquitty we see in the market. Studies have shown investors have 30 more in their Bank Accounts as a result of the automation. As we look ahead, theres four points i want to discuss. First, global competition to adopt the latest a. I. Technologies will make human Decision Making more efficient in terms of speed, processing time, depth of data and its going to confirm more efficiencies and costs for u. S. Investors across the board. Competition in the markets has resulted in online trading, and weve seen a rise in the etf industry from those efficiencies. Automated trading has brought down overall trading costs to a fraction of the price from decades ago. Second, we can expect to see a prolifation as a. I. Becomes valuable. A. I. Functionality includes monitoring reporting and compliance and processing of regulatory filings, loan origination processing, detection and reporting of illegal trading and detection of cyber risks. I want to point out through Public Private partnerships which can play a role to share those limited resources in a. I. And to share cutting edge technology. Since 2019, several markets initiative markets have welcomed the opportunity to Work Together with finra for contributing the know how, welcoming Artificial Intelligence. We, too, can be the victims of fraud. If bad actors become more sophisticated globally, its vital that regulators have the resources so they have the technological capacity and access to a. I. And automated technologies. Third, as a. I. Technology matures, we can expected increased demand for high quality robust data, including alternative data to provide what i call the crude oil for the engines of a. I. This intains large quantities of complex data that humans alone cannot digest. Were going to see policy questions arise around this proliferation of data. I think it was noted questions of competition and antitrust in the digital marketplace. Were going to see increasing discussion of intellectual Property Rights. I think alternative data has been skuccessful. But i think we need to continue discussions surrounding algorithmic bias. In my prepared testimony ive noted next steps, including industry led initiatives to utilizeathyi utilize ethics approaches. A. I. And automation can and should be a tool rather than a replacement for humans. Some jobs will disappear and others will grow. Areas of growth we can expect to see are in the computer occupations, jobs related to the transmission, storage, security of privacy and integrity of data. The fiber optics industry. Theyre going to be fuelling the a. I. Economy. Theres massive demand existing for qualified technological talent across virtually all sectors of the economy, particularly in the Financial Sector. The current baseline participation for women and particularly a woman of color is something that leaves room for substantial improvement. And a Skilled Workforce for tomorrows wall street is only as good as the companies there to invest in technology. I thank you for your time. I yield the time for the next witness, thanks. Thank you. And youre now recognized for five minutes to give an oral presentation of your testimony. The button. Oh, the button is not the microphone is very directional. Rotate it straight at you, it helps. Thank you, chairman foster, and Ranking Member for the opportunity to testify on the impact on a. I. On the Capital Markets. Many people associate a. I. With high tech. But we strongly believe we can use this technology to target the fraudster. As you know, nasdaq has experience leveraging technology to opprate the markets and markets around the world to protect participants. We operate clearing houses around the globe, and we sell to hundreds of markets, exchanges, and broker dealers. Our Surveillance Department is monitoring the markets inside the trading, fraud, and manipulation as well as handling realtime events in the market. The accessibility of the markets and increase in players with the ability to deploy many strategies using their own intellige technology can act as the perfect eco system for market manipulators to hide amongst the noise. This increased complexity created new problems. Our program is using coding to detect unusual market behavior in realtime utilizing over 35,000 parameters. In addition to realtime surveillance, there are over 150 patterns covering surveillance that identify a wider range of potential misconduct. The team proactively develops tools to increase the quality of surveillance and to meet changing demands in the market. With the manner in which patterns are recognized, it can be difficult to capture new behavior and to remain proactive rather than reactive. It can limit the results depending on how they are capbrated. Calibration presents a continued challenge when determining the best balance between false positive and true alerts. These challenges led to a collaboration between the intelligence lab, technology busine business. Using a. I. To detect abnormal behavior patterns is based on the notion that behavior can be identified by signals in the market. The scheme to defraud Market Participants has a specific pattern to it. It is declined, an action is taken and the trading is done back to norm. By leveraging a. I. , detection models are not tied to static logic or prarameters. Were able to train the machine based on visual patterns of manipulation. And we started to look at the spoofing pattern. The machine was trained with human input, and then transfer learning was used to expand the project beyond spoofing. Transfer learning leveraged a. I. To develop a model as the starting point. By using deep learning and techniques, the new models for detecting, indicated results with 95 fewer examples. The use of a. I. Will allow us to focus in depth investigations on behavior instead of triaging false positives. Continuously training the machine to produce more and more accurate outputs. Billions of messages pass through a larger market on an active day, in addition market abuse attempts have become more sophisticated putting merpressume more pressure on the team. We are sharpening our Detection Capabilities and broadening our view of market activity to safeguard the integrity of our Financial Markets. Surveillance is a critical use case for a. I. But nasdaq is looking to apply it in other business. We are using a version of a. I. , natural language processing to facilitate the compliance review of Public Company filings. In closing, were convinced these use case will benefit investors and the resiliency of the u. S. Market and the other markets we serve. Thank you for the opportunity to testify and im happy to answer your questions. Thank you. And well now recognize myself for five minutes for questions. I should mention to the members present, it looks like the latest estimate for votes are now 11 30. We may, in fact, have time for a second round of questions for members that are interested. Well have to play that by ear. Doctor, you note in your testimony that the data vendors off a wide range of sets. Other witnesses mentioned that. Things that were not available a couple years ago, and not only the data itself, but the Processing Power to analyze it and the realtime delivery of that data is becoming more and more important to successfully trade on it. Could you just illuminate the Interesting Data sets you see being used. Its combination of data sets. On one hand we have access to credit card transactions, geo location data, satellite images, transcriptions from earning calls. Engineering data, like, production companies. It allows us to estimate better for extraction of oil or fracking. Also some data. Keep in mind, please, 50 of all data recorded today was inherited over the past three or four years. Going back to history, going back to mesopotamia, theres a lot of data around data were not aware of. All this data can be used to understand what is the psychology of people, the state of mind of people, understanding people are more inclined today to take risks or to for instance, relocate their assets to fixed income instead of the stops. Try to understand from news articles as one of my colleagues mentioned what are the narratives associated with particular companies. So the amount of data today is staggering. This is only going to increase because the storage of data is becoming cheaper every day and the Processing Power is increasing. This is definitely a trend thats not going to stop. And know that, as i think i mentioned in my opening remarks, that is a danger of driving monopoly. The returns to scale, because you get more correlations to look at with your ai if you have the full range of data. This will naturally cause those smaller players in the market to not be as effect. I think thats part of what youre seeing in High Frequency trading, the consolidation youre seeing there. Is there any way around this . Should we how hard should we lean against the natural tendency to monopoly here in financial trading. There are two schools of thought in this regard. Number one, there are a number of academics who believe this is not necessarily negative in the since that the few survivors that are able to consolidate High Frequency trading are operating like utilities. Essentially what happens is they break even. These technologies are becoming sore expansive they have to spend this time and money in order to achieve a profit thats dwindling. Theres a number of academics c is not necessary. Its also could cause a domino effect if one of them fails to provide liquidity. There is an need to strike a balance between on one hand preventing too much consideration and on the other hand favoring competition between these operators. You mentioned that this actually netted out very positively for someones retirement account that it actually because of the lower transaction costs, i think you quoted 30 more in your retirement account as a result of this. So similarly, when ai is widely deployed, if its effectively deployed in principle weve got a more efficient Capital Allocation across our country. Is actually the best strategy to let a small number of very dominant players have access to all the data set to get a more Efficient Company and or are we better i think its absolutely vital that we encourage policies that promote strong competition in the space. With automated trading, weve seen such fierce competition over the past decade or two were approaching near zero latency speed but also more monopolization. I think my time is out here. Its my intent to return. Thank you. Id like to remind all the witnesses to speak as directly into the microphones as close and as loudly is comfortable for you. All right. I yield five minutes to the Ranking Member. Thank you, mr. Chairman. Ms. Wagner, as you know, the s. E. C. Has experienced some cybersecurity difficulties especially in the 2016 edgar data breach. I think its important for the s. E. C. To only obtain proprietary algorithms if necessary with a subpoena. Why is it important for short code to be protected . Thats a very good question. So the real life blood of automated trading and the secret sauce is the source code. Thats the valuable intellectual property that the different firms are competing against each other with. Not just domestically but globally. Just like a selfdriving car Company Needs to keep its algos and source code, intellectual property protected from misappropriations, so there was a proposal to perhaps collect ip source code and put it in a government repository. That never came to light but thats something were educating policymakers on. I think it should be a bipartisan area of interest to insure we have a globally competitive marketplace that protects i appreciate that from my time in the military working in intelligence. We had a principle we lived by because of the sensitivity of the data we collected and maintained. If you dont need something, dont keep it. Which means if you dont have to protect what you dont have. And my concern is the how vulnerable the industry becomes because quite frankly, the government tends to be the weakest link when it comes to Data Security in some aspects. So i think obtaining that source code is not only just a violation of the privacy right of the business, the coder. It could be a National Security risk. Is that i think thats right. If bad actors were able to breach the source code, it would be representing an opportunity for manipulating the markets or cyber risks. Its vital that we protect the rights of source code. Thank you. Ms. Fender, the adoption of Artificial Intelligence and electronic trading can disrupt the job market in this space for traders but technologies aults create a need for more workers in other fields. Today, we have about a Million People working in the airline industry, but in the early 1900s, the Washington Post let a headline that said man will never fly and shouldnt. Part of their argument was the displacement of people in the job market. Could you touch on the job fields that are growing because of the use of a. I. In the Capital Market space . Yes, thank you. Thank you. As you noted, there are many ways that jobs are changing. Adaptation is the key. When we surveyed industry leaders, the people that are doing the hiring. What is the most important skills Going Forward . Maybe its not necessarily the job description. What are the skills underlying who will succeed in the future. They talked about t shaped skills. This is the idea if you think about the letter t, the vertical bar, deep subject matter expertise and a horizontal bar where you can cut across different disciplines. If you think about we have big rinsks. The ability to connect the two is where theres a lot of opportunity, and so these are the innovators. Youll see more research needing to be done so we understand what the trends are. The key thing is that people have to ask the right questions. Firms are realizing you have to think about the roi of gathering the data. Many of the Machine Learning people say a large percentage of the data isnt that useful. You have to be smart about how to do that and start the process with investment professionals. Not all the jobs are going to be able to code, but there are some that come about . Definitely. We dont think for example that all charter holders need to be programmers, but they need to have data scientists on their teams. They need to speak the language and Work Together. Okay. I want to talk about the use of Artificial Intelligence and fraud detection. I view cybersecurity as the biggest challenge we face in the nation, both from a business, government and personal perspective. Can you touch, real quickly, were running out of time, how algorithms are used to detect unusual behavior. We lie on it to identify patterns. So we program things to pick up on the unusual things based on historical comparison on specific stocks, how theyve been trading in the past. Thats what we do already. The new thing here at this point, i think well leave that to hopefully to your next round of questioning. The gentlewoman from north carolina, ms. Adams is recognized for five minutes. Thank you very much to the chair for putting this together, we appreciate it. Also, those of you who have come testify, thank you for your comments and for your work. Automation technologies which enable the transfer of task from human labor to machines affect approximately 6. 4 million workers employed in the Financial Services industry. Specific Industries Like credit lending and Capital Markets are being affected by ai as humans involving Data Analysis, Decision Making and compliance are replaced by Machine Learning robots. The shift and job automation could predict which jobs and official services will be replaced and what new jobs could be created. Ms. Wagner, specifically examining loan underwriting compared to the traditional methods of meeting a loan application in person. To what extent does ai replace or augment the work done by loan officers, credit counselors or other credit underwriters. Thats a very good question. So in the Consumer Lending context i think its very important that ai is a tool for humans, whether extending credit and loans that there are systems in place to insure there isnt any sort of bias. And in my prepared testimony, i noted some suggestions our members are not ingaengaged in Consumer Lending aspect. I think Loan Companies individually could employ ethics officers to insure there isnt bias in the lending context. Its important that industry members share Lessons Learned as they explore how theyre democratizing access to credit. Finding the most efficient ways to extend the credit. Its vite tool make sure that we minimize the risk for algorithmic bias. I think its very vital. Thank you, maam. Is the u. S. Properly equipped to remain competitive in the workforce. This question is the doctor and ms. Fender. The u. S. Is the leader in the official Services Industry today. My concern is that the leadership is being challenged by the fact that on one hand, we are not investing as much in ai as other countries. Number two, the fact were educating our competitors. Im very concerned the innovators of the future are attending today a class in our universities, but they will not be allowed to stay. As a result, yes, were very competitive and this competition this ability to train the skills is going to turn against us if were not able to retain this talent. Okay, ms. Fender . We have seen that, again, its early days for how this changes our industry with only about 10 actually using these techniques. But what were seeing is that, you know, firms are doing ai labs. Theyre doing innovation hubs. They realize this is something they need to be proactive about. Were seeing out of our case studies, you know, we had a criteria that things in our case studies had to actually be in practice. Theres a lot of talk out there, but things that are actually in practice, five of the nine are here in the u. S. Great, thank you. Doctor, are we adequately teaching the skills needed for the jobs of the future . Thank you for the question. I think were adequately teaching those skills. I think the questions about who has access to that teaching. So when you think about underrepresentation of certain individuals and members of the workforce who are not getting the types of education that are needed for the jobs that may be coming online as a result of automation and Ai Development. And so i think if we were to have a full pipeline of folks that are able to receive what it is that we teach in our colleges, universities, even high schools and younger, then we have to be more proactive about making sure that all people have access to that teaching and information. No one left behind . Absolutely. All right. I appreciate it. Im going to yield back, mr. Chair. Thank you very much. Thank you. And the gentleman from indiana, mr. Hollingsworth is recognized for five minutes. I appreciate each of you being here today. I appreciate the chairman for holding this hearing. Its an important topic, something ive been passionate about since arriving here in congress. Doctor, i appreciate your comments. Because what youve touched on is something that i have been an ardent believer in for a long time. Number one, that the big arm of the federal government isnt going to stop the growth of the technology. Isnt going to cease the investment in ai, here or around the world. While we can shape the context by which that technology flows, were not going to dam up and stop the technology. When people say job losses may result on account of this, right, theres a lot of fear, a lot of desire to put an end to that and to stop that. But i like how you referenced a lot of training and retraining that may need to happen. Training individuals that are graduating from school to make sure they have the skills necessary, but also insuring those that are already in the workplace have the opportunity to get the retaraining. As we see further growth and development in ai it will require more and more frequent retraining to stay ahead of that, to stay relevant in that field. Thats a very competitive field, right . The second thing you touched on is something im even more ardent about, a lot of ardor expressed. But is we educate a lot of kids in this country. We do Higher Education in this country better than anywhere else in the world. We bring a lot of talent into this country. We invest a lot in those kids and we politely ask them to leave at the end of their tenure here. Thats embarrassing, idiotic and stupid and i hate that. I want to retain talent in this country. I believe this country can provide a crucible for Technological Development you cant find elsewhere in the world. I think it will benefit humankind overall. I want to make sure we do that. I appreciate you touching on those topics. I appreciate the investment. Ms. Wagner, i know you have a source code event today, yesterday, tomorrow . This afternoon. This afternoon. Right. To talk about source code appr educating a lot of people about how important that is. Where i go in indiana, i hear more and more about how much technology, how much investment, how much ip is in things that arent readily seen, right . Either in business processes in the source code, in the technology, underpinning automation itself. So i know how important that is. I really appreciate you bringing that to light. All that being said, i wanted to ask ms. Ratio a question. I that is maybe a little bit far afield from what were talking about. I had some people in my office earlier this week that were complimentary of nasdaq surveillance services. Very complimentary of the Republic Companies and how when something seems amiss in the markets the nasdaq was quick to pick up the phone and Say Something seems amiss, lets figure out whats going on. One of the things thats important back home is bio tech. A lot of bio tech firms were trying to get the word out about it theyre concerned about market manipulation, specifically with regard to short selling. They are promoting the idea there should be more disclosure around short selling. Similar to many long positions. Now they came in and said that disclosure around short selling would help us as a firm better understand those that might have interests adverse to us, because we cant really track that right now. But the counterargument they made was gosh, nasdaq seems to be doing a really good job of figuring out when theres potential manipulation. I wonder if you might touch on that, is disclosure in short selling something that would benefit the market, something that would benefit the firms or do you feel like you have enough the ability to track potential market manipulation on the back end . Again, im not against short sellers. I just want to make sure its legitimate action, not market manipulation. I wonder if you might comment on that in the last minute. So, i think this is a big part of surveillance, right . Its information is always needed to understand what is happening. I do think that what we have today is sufficient, as you say, we have a lot of patterns that are detecting manipulation such as short selling. I might say the selling is legal. Ing right, of course. Its really to protect how its used in a you feel like you can detect the activity that would be illegal adequately. The question is what do we do with it after that point is where we should focus Public Policy attention, is that fair . Yes. To be fair also, there are other parts that handles more of the policy questions. Okay. But for me, as a surveillance practitioner i think the tools we have to monitor the markets are adequate . I think thats an important question. When they were in my office, the question was where do we need to focus Public Policy attention. Maybe its focus on the penalties. With that, i yield back. Thank you. I am very encouraged one of the areas of bipartisan agreement here is the insanity of the business of warning people their phd diplomas and pushing them back on an airplane. Thats one of the reasons i was provide to introduce hr4623 that keeps talent act, designed to just exactly solve this problem. I look forward to my colleagues support on this. I would like to recognize ms. Garcia for five minutes. Thank you for holding this heari hearing. Thank you to the all the wi witnesses, good morning. I wanted to focus on the issues some of you have already talked about. Like ms. Adams im particularly concerned about jobs. My district is from houston. And 77 latino and its also working class. So were always concerned about jobs and im encouraged you all seem to have the consensus that there will be job displacement, that there will be new jobs created. My main concern, of course, is whether or not we do have the skill sets to transfer those skills or make sure we can fill those jobs. Because in the end thats what matters to families in my district. Im also concerned with automation and the difference between ai and automation and how it can Work Together. Specifically in the area of Regulatory Compliance. Ms. Fender, in your experience, has ai and automation affected institutions . Is it improving . Is it still work in progress or how are we doing . Thats a very good question. Its still kind of early to know, right . We hear so much about what is coming, and yet, you know so compliance areas are growing in firms. Now we have more and more data. And regulators are going to be able to have the same sort of data. The question is, is there a greater risk of Insider Information now . You collect more data and people can see lots of different patterns out there. If they see that and can trade on it before the market, then you have challenges for the s. E. C. I think in terms of reg fd and so forth. Okay, ms. Wagner can ai be used to simplify and insure Regulatory Compliance with the federal agencies in charge of supervising the Capital Markets . I think as the data sets become more complex, i think its going to be vital that the regulators have the resources to have their own ai either independently of the companies or together with the companies through Public Private partnerships as the bad actors become more sophisticated and were talking about global actors. We need a strong beat here in the yus. Its important that the private sector Work Together with regulators to insure they have the resources. Because you know, the symptoms are becoming much more complex and we need to keep up with the pace of that technology. I think that is a big concern of this committee. Its those bad actors as you described them. So how can ai assist us with antiMoney Laundering compliance or suspicious activity reporting . I mean, are we wellprepared for that. I know we did a code out to several countries. Things are getting more and more sophisticated. It seems like the bad actors have more money and better things, you know, to find ways to hide the money. Do we have what we need to detect it and insure that we can catch it. Its vital we focus on this and i would say the new head of innovation at finra has an excellent group. They just established themselves this year. Theyre a fantastic resource. Working together with other regulators are private sector participants to gather information about best practices and to make sure we have the best technology. This is hundre100 something we to be focused on. Do you think our regulators and our oversight entities are wel wellprepared in this arena . We need to be investing in technology. Theres always room for more technology. Its a constantly evolving space as everyone noted. Weve got to keep very much on our tip toes on this and keep on investing in this area. All right. Did you want to add something . I think the more data we have, the more complex it gets, right . One of the other things were concerned about is the Investor Protection side. Bad data goes into these models, they can be marketed in many different ways. Disclosures are really important. So, you know, understand your clients, understanding where the money comes from and understanding what clients are really getting it all goes together. Thank you. Both of you, i yield back. Thank you. The gentleman from virginia is recognized for five minutes. Thank you, mr. Chairman. I want to thank all the witnesses for being here today. Im so happy youre here. Im not showing any favoritism. I would particularly like to welcome ms. Fender from the cfa student which institute. They provide a host of resources for professionals who work in the Financial Services industry who are among the most qualified in the financial industry. I am honored to have such a distinguished group reside in the Fifth District and although ms. Fender is not a constituent herself, her organization employs many of them. Welcome to all of you. Ill start with you, you probably knew that was going to happen. Can you talk about how cfa is adapting a charter to these ai and Machine Learning innovations in the investment industry . Thank you very much. Pleased to be here representing virginia. The Cfa Institute is really the global standard for investment practitioners, right . The people that have our credential are the Portfolio Managers for your 401 k . The chief investment order at the public pension fund. People that are really safeguarding the Financial Futures of so many people. Its imperative for us to keep up to date on what we teach. I mentioned earlier in my testimony that weve just added Machine Learning into our curriculum. This is a significant indication that we are seeing the market change. We need to prepare people. And so we have a group called our Practice Analysis team. And theyre out there all the time going to these conferences, figuring out what is the next thing that people need to know. Global demand is growing, and especially for those who really combine both competents and ethics. The reason i ask the question, so my prior job, we talked about monopolization of data. I want tod monopolize as much data as i could when i worked for the office of the secretary of defense. We had to look at all data across stove pipes. To see how it includes that data or to aggregate the data, analyze and execute that data based on how we templated Human Behavior. Ill start with ms. Ratio. Im going to ask you these. This is the exciting part for me, the technology part. Do you see when we did this, we had multiple data sets people never seen before. We talked about the challenges of data. Multiple data sets and data we had sort of aggregated and combined with other assedata se. We thought we had the right answer and we didnt. Do you think thats something youre going to see more of in the future, is that there wont be a human in the loop and it will be more sort of human templating or Machine Learning rules to mimic what Human Behavior does with sets . Looking at analysis or fraud or anything of that nature. I think were a long way away from that. For now, the way wree do it is really to have the data that we have. For us, its the order that we already have. Now were just applying a new technique to give us more better overview thats not that parameter driven. I really think the human in the loop is the way to go because there is much more analyzes that needs to be applies after the output has come. When we saw we actually thought we could take the human out of the look in some of our processes and found out it was not a good idea. I see some head nods back there. We tried to do it. Doctor, you were talking about there could be some advantages to, you know, sort of aggregating as much as data in one place as we can, right, filling in the gaps of that data. Thats what ive been trying to wrap my arms around. My job was to monopolize all the someday, right . To use competition to give us the best solutions we could, for first second third order effect for a specific part of the network. This is a tough question. You know, to be this objective in 40 seconds is going to be ridiculous. But when youre looking at this, do you think that with all the proprietary technologies out there, do you think there will be a voluntary sharing of that data if we find something thats very good across multiple sets do you think well have that type of sharing for proprietary Solutions Based on algorithmic types of analysis . Do we think we have to force that to happen when we monopolize that kind of data, if that makes sense. Are you referring to sharing these technologies . Yes. There had been a lot of transfer between the agency and various contractors. So that could be a model that could work for the s. E. C. In particular in my remarks i mentioned the crowd sourcing of investigations. How companies or private participants could help agencies identify market manipulators. The gentleman from illinois is now recognized for five minutes. Thank you, mr. Chair. Thank you all so much for coming. I had a back in my prior life i had a head of engineering who had a theory ive yet to prove wrong. Every advance in Technology Gives us more precision and less knowledge. He started with slide rules where he had to know the order of his answer. And you know, of course, in my lifetime weve got from foldable maps to gps that can give me the exact longitude and latitude and i cant tell you if im north of west. Ai has struck me as putting that acceleration on steroids. And the one point i built a genetic algorithm to predict the revenues of our utility business and it was amazing. I cut our revenue forecast variance by 90 . I have no idea how it worked. And you know, thats the power and the frustration. And i mention that because i think most of you have talked about the consumer benefit that comes when we get all these algorithms out in the markets and we get trader costs. Thats all terrific. The question i have a lot of you have also talked about bad actors. We can put up monitoring for that. The concern i have is the tension between the transparency of the model and whether the model can actually effectively replicate a bad actor we dont understand. Its fairly easy for me to imagine a trading algorithm that is tracking data and betting on one country invading another. I can imagine changes in currency flows for illegal activity thats not illegal but is arbitraging some spread. I wonder if youd comment on that tension between transparency and algorithm. And to whatd degree we have or need regulatory tools to stipulate where we sit on that continuum. I think transparency is absolutely vital. I think its also very vital that regulators and the exchanges have the resources that if they note any sort of irregulari irregulate irregula irregulatety in the markets, if theres a detection of illegal activity then the regulator if i can clarify. First would you agree the more transparent the algorithm, the less powerful. That transparency is vital. If were talking about intellectual Property Rights in the source code of the algorithm, thats proprietary information by transparency, im not referring to whether or not the public has access to the algorithm. Im referring to if our brains can understand i got it. You couldnt understand what its doing. Sure. That question becomes more complicated in the Machine Learning context. Especially you point to an interesting question. As the commands become selfacting in a way that theyre basing the analysis of the existing data sets. I think thats what we need to answer to. Its an interesting balance. So question, this is for you, but really for all the panelists. I think thinking about that problem before it gets there, because it strikes me that the there will be a pressure for every trading firm to develop the most powerful algorithms which are going to be the ones that we have the least ability to unpack and understand. I think this is an important question that industry should get together on and share the best practices. How do you balance that anybody who thinks theyve got a great answer on this. Number one, how should we do that . Number two, to what degree do we need to coordinate internationally . Even if we do anything in this country, because all these markets are so interlinked. Is this a u. S. Problem or an International Problem . Anybody have thoughts on it . If i may, these are very important distinctions. Black boxes tend to be less reliable transparent solutions. Particularly in finance because were dealing with problems with the signal to noise ratio is very low. Why is the ratio low . Because of competition, because of arbitrage. Almost everybody would be able to extract profits from the market. The solutions can identify patterns that are more real. Theyre just patterns in the mo noise. Combine the noise patterns with signal. One solution would be for investors to understand carefully when a product is based on a black box solution. Thank you, i yield back. As i mentioned, were likely to have another round for members that are interested here. The gentleman from missouri, also the chair of security of International Development and Monetary Policy is recognized for five minutes. Thank you, mr. Chairman. Really appreciate you calling this hearing and we appreciate all of you giving us your time. I dont know how were going to deal with ai and human beings. Long before we had flip phones, captain kirk had one. And long before we had the smart watches, mr. Spock had one. A lot of attention has always been paid to hollywood, particularly in Science Fiction and the military. Our own military. So a lot of people have their eyes on a fearful future as it relates to ai. And to be straight, im one of those. You know, i know we cant hold back the wind. And its inevitable were going to see more and more of this in the future. Im not sure they ought to try to hold it back. To the degree we can control it, thats what i think we ought to do. Thats where im concentrating most of my interest. Doctor, i thank you for being here. But im wondering, how inclusive this new technology is right now and what can we do to make sure that in the future that every component of our great mosaic in the United States is a part of it . Thank you for that question. I share a little bit of your fear, because what we know persists as technology changes, as technological changes are made is that some people typically the same groups of people are left out, left behind, are disadvantaged. So even as technology is unpredictable. Some of those exclusions are very much predictable. I think those exclusions are present and recurrent as most of the folks on this panel have at least alluded and nodded to. When we look at our Technology Sector, those who are prepared to be part of that sector. Those who are currently working building the technologies of today and tomorrow are tremendously unrepresentative of our full democracy of all the citizens of our country. I think representation makes a tremendous difference. I think the place were in today with respect to some of the inequalities and devastations that technology can have will be beat f built for what purpose. So i think moving forward we have to change that. That is, we have to invest strategically in building a more inclusive workforce in these sectors that are growing. That is the Technology Sector in the Financial Sector as well. What do you think we should do or any of you do right now if we we have to have young people interested in and committed to the future and ais inest efbtable part of it. What should they do next week . What should young people be doing . How should we direct young people right now who are scientifically gifted. What should we do . Promote responsible innovation. Our members support trying to get out there to the middle school students, a diverse population of people, get them interested in stem fields. I think there is a lot of opportunities for companies to partner with some of the schools in a geographically diverse part of the country and help fund that. Recruit now. Kids get interested from a young age. Weve got to get in there early and make sure kids see role models at those firms. Thank you very much. Thank you. And now i guess we have time for a brief second round of questions here. Weve had sort of two different narratives that have been going on here, one is the optimistic narrative of the i guess the tshipped skills or human intelligence paring, human augmented intelligence. And then the intermediate way of transfer learning where youd use one field of expertise and transfer that to another field thereby replacing multiple human machine pargz. One example was from the geniuses at goldman using satellite data to predict analyzing satellite impaging to predict cement pricing in the future. And then potentially using transfer learning so that knowledge could be transferred to Copper Mining or whatever else it was. On the other hand, theres an alternative narrative, you aggregate all the data you can and say, i want a general purpose learning, trading algorithm to look at all satellite data and look for all market correlations. That would detect the cement market, look at the parking lots of toysrus to predict they were going bankrupt because they didnt have much cars. This could be written to deploy he tens of thousands of machine human pargz here. So which of these two narratives are going to end up winning and how is it going to net out for human participation . Anyone who wants to tackle that tar baby. I can start and say that one of the found daigtsal concepts in investing is correlation is not necessarily causation. We have a lot of date awe, see the patterns, but you need a human to ask, whats the right question . I mentioned the example of going through the news stories with bloomberg. They said the question question was to go through and not say, what do we think the author of this article wanted to get across . But what do we think people are hearing . There are a lot of nuances about how this is going to play out. Thats why again, having sort of a collective intelligence and diverse perspectives, is going to be important. Doctor, do you have a yeah. I think that the two narratives have some part of truth. I think in the shortterm, we have reasons to be worried in terms of the transfer of knowledge and the potential displacement that will occur as these technologies are more broadly deployed. But i think in the long term we have reasons to be optimistic. Because the next generation will be better prepared than our generation, previous generations. It is very important that we give access to education, equal access to education. Its very important that we encourage kids to learn how to program, participate in math and engineering classes, and that we form a flexible workforce, a workforce that in the future we dont know what these technologies will do in 20 years, that they are able to engage proactively. But is there a danger that this is going to squeeze all the profitability out of Financial Services . That if you had complete knowledge of everything and very efficient algorithms immediately trading on that knowledge, the 30 improvement on your requirement savings, all of that used to end up in the pockets of people with nice homes on oyster bay. And thats sort of the nature of things. It may be that when we get this much more efficient economy with the extensive deployment of ai, the total amount of money will continue to go down the same way High Frequency trading is suffering that. One view if i may, in fact having such perfect market is not necessarily bad for society. Meaning that the day that we go to our Financial Adviser and we receive the same treatment that we weve when we go to the doctor, essentially, there is a protocol of this is what you need to invest in order to achieve your retirement goals, i think thats a good outcome. As we see greater efficiencies, robo advisers and others, more Asset Managers, will be able to deploy that to the masses. It raises a global competition question. Were not just talking about competition domestically. We are not going to stop time all across the world. Other countries are innovating in ai. Its inevitable. Well compete in that space and we want to keep the u. S. Markets the envy of the world. So if the future of Financial Advising is conversations with alexa, i guess it comes down to, you know, is the objective that a function that the ai running alexa is maximizing, is that amazons profit . Or is it some linear come nigs of that and diversity, inclusion, a secure retirement rather than steering people into products that are profitable for amazon . I think the vital part, we have competition, theres not too much aggregation of power in one entity. We need to have policies that promote robust competition amongst the robo advisers to make sure there is data accessible to not a barrier to entry. This is going to be an exciting space where financial meets judiciary, meets commerce committee. Were all finance is becoming more technology and technology more finance. These are the right questions. Thank you. And ill yield five minutes to the Ranking Member. Thank you, whether chairman. Miss horatio, id like to go back and continue our conversation that we were talking about. Cyber security and using ai and fraud and i wasnt very well managing my time before. So could you explain further how nasdaq is using ai and fraud detection . Yes. I think its important just so start that the future is here. Right . We have billions of data points. Its a massive amount of data that needs to be analyzed to capture anything that is then fraudulent or manipulative in the market. We have that environment already. What we have been doing so far is deploying algorithmic coding to be able to process all of this data very fast. Our realtime surveillance is picking up on unusual behavior within seconds after it has happened in the market. There is a fast and efficient way. But as it is growing and expo nevlly growing, there is the need to continue to invest in other ways of looking at it. Where ai then comes in. Its more a broader approach, and it doesnt have to be so parameter specific today so we can capture more things that are more sophisticated. As we have been talking about, its not only us using this technique. The participants in the market are using it as well. I think its important for us to match their technology with ours when we look at the types in the market or the behavior. Thank you. Doctor, can you touch on the differences between automated trading with algorithmic, high freque frequency, and computer, how theyre not the same and what differentiates them algorithmic consists in following rules, computer follows some rules to achieve an outcome. It does not require Machine Learning. Machine learning it the learning of patterns from a set of data with us directing that learning. Essentially what happens is you give to an algorithm a data set, and the data set identifies a pattern that we were not aware of. What was the third . The automated trading . It can happen with or without Machine Learning. In the earliest stages 2005 High Frequency learning occurred without intelligence. But today liquidity providers, hedge funds, deploy these with Machine Learning embedded. Thank you. Miss wegner, weve had some discussion on the cost savings that have resulted from ai and automation in the Capital Markets. Do you see that these efficiencies are a significant reason behind the record returns inverts have enjoyed in the last decades . This has contributed. Every reduced cost of trading adds up with compounding over time. As the markets become more efficient, investors are going to have more. Whether theyre like half of americans invested in a 529 plan or otherwise, its been a net positive. I have no further questions. I yield back. Thank you. And the gentleman from missouri is now recognized for five minutes. Thank you, mr. Chairman. Im interested in, you know, how do we do planning now for the future . For example, we just updated our antiMoney Laundering bill or the bank secrecy act. And im sitting here now, and i introduced a bill, so ive been feeling pretty good about myself until you guys came up today. And im thinking, why did we go through all of that . You know, because the bad guys are out, you know, trying to figure out how they can, you know, exploit whatever we pass legislatively. How do you see ai involved in antiMoney Laundering efforts like the legislation that we hope to send will take up during our lifetime . Is there any way you think that that can play a role, that ai can play a role in Money Laundering bills, how we are trying to reduce it . We are never going to probably eliminate it. Well, this is a gargan taun problem. We have to tackle tremendous amounts of data, and identify this needing in the haystack. I think a practical solution is for regulators to Work Together with data scientists, with the entire community, and crowd source these problems. We need to analyze this data and give it to the community so the Community Helps us enforce the law. Of course they can be rewarded with some of the fines levied against wrongdoers. But i think thats a very doable approach, given number one how difficult it would be for the agencies to develop the techniques that the wrongdoers are developing, and number two the amounts of data that we need to parse through. We had the treasury secretary before our committee yesterday, and of course i didnt even raise this issue. We had an agency, fincen, which is an investigatory part of the department of treasury. And so im here wondering what theyre doing, trying to keep up with the technology and what challenges theyre going to face in the future. And so, you know, you guys have destroyed almost everything i was proud of. But we appreciate you coming here anyway. Thank you very kindly. I yield back, mr. Chair. Thank you. And id like to thank our witnesses for their testimony today. Without objection all members will have five ledge laytive days to submit written questions for the witnesses to the chair, who will forward them to the witnesses for their response. I would like to ask our witnesses to please respond as promptly as able. Theyll have five days to submit extraneous materials to the chair for inclusion in the record. This hearing is now adjourned. Coming up, well talk about the future of diplomacy and defense in afghanistan. Live coverage starts here on cspan 3, online, or listen with the free radio app. This weekend book tv features three new books. Saturday, scott adam, creator of the comic strip dill bert and author of loser think. Were all elevated in our opinions because the news model is forcing us toward more pro vocative stuff. Where before they would have said heres the news, theres my news, now its replace the enter statement. Retired navy admiral and formeral supreme allied commander discusses his book, sailing true north. I would go into combat in afghanistan all tricked out with my bulletproof everything and my helmet. Guys on my right and left with big guns. I was actually pretty safe. Next to me would be someone like Richard Engel from nbc news stand tlg in an ilfitting bulletproof stlaeft i assure you wouldnt stop a bullet. Hes got a tin cap hat on cantered off to one side. Hes risking his life to telling us whats happening. You think hes serving us . I do. Then sudden on afterwards, New York Times contributing writer lindy west talks about the me too movement and her book, the wichds are coming. Shes interviewed by author rebecca traceter. People are always asking me, whats the path . We have to have something. Ive been trying to come up with an answer because people keep asking me. I realized that the answer is, how about if, i dont know, not my responsibility to figure it out. How about you workshop it, trobl shoot, keep trying stuff until people forgive you. I dont know. How about you figure it out . Watch book tv everybody weekend. The House Judiciary Committee meets monday to hearing evidence in the impeachment inquiry. The chair and Ranking Member will hear from democratic and republican counsel on the findings from the inquiry. Our liveo