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In just a few months, artificial intelligence (AI) has certainly exploded onto the stage in a way that has surprised many. Take, for instance, the mass popularity of Chat GPT, GPT-3, GPT-2, and BERT. The scale and intelligence, with the advancement of computing power with large data sets, provide fertile ground for AI to take off.1,2

For us in medicine, we are used to applying approaches to diagnosis and treatment that are rooted in deep understanding of disease processes and informed by critical appraisal of evidence-based strategies and experience over time. Medicine has adapted and kept pace with the various emerging technologies and, as a field, has reached many advances.3 Part of the heuristic and epistemological approach is that technology has always been a tool to be applied to the medical process.4

Agency and control have been at the forefront of how we use tools. However, with the introduction of tools, there was some initial trepidation. When one looks, for example, at the evolution of different tools over time, in some ways, every tool has brought on some initial anxiety and fear. One can only imagine the angst of a painter with the emergence of photography, and yet, painting and art have not been displaced.

The emergence of AI has generated much for even those embedded in the technological field. An approach to machine learning and artificial control intelligence should probably stem from an understanding of what it is and what it can do. In taking this approach, we are positioning ourselves in a way to inform industry and help solve problems that are meaningful with an ethical and value-based framework.

The emergence of technology and its adoption in society has brought on various emotions in its adaptation. A number of researchers have explored this area . One particular Model is Gartner's Hype Cycle, whereby new technologies are followed by an up-peak of excitement, followed by a disillusionment phase, and then a normalization phase where one understands the utility and limitations of the new tool.

Another heuristic to understand emerging technology is through an economic perspective. The Kondratiev Wave theory describes economic cycles in the economy and links them with technology. Another researcher in the field of paradigm shifts, Carlota Perez, defines technological revolution as a powerful and highly visible cluster of new and dynamic technologies, products, and industries capable of bringing about an upheaval in the whole fabric of the economy and propelling a long-term upsurge of development.

It is quite astounding that a machine can read large amounts of data and emulate and identify patterns, but, at its heart, not quite understand what it is doing. So, although technology can incorporate an immense amount of knowledge that is often cultivated over many years in a rapid time, it still has challenges with reasoning.

For us in the medical world, it is hard to imagine a system that emulates what we do: Refine the diagnostic process and apply knowledge to patterns based on genetics, epigenetics, life experiences, and responses to various medication therapies, and then fine-tune this to each patient while seeing it from the individuals perspectives and values.

So, one may ask, what is the concern? A recent letter from several technology leaders spoke to the concerns around the rapid deployment of AI.5

In some ways, these technological innovations have always had human beings behind the controls. What is currently challenging and concerning for various individuals, including those in the fields of computer science and engineering, though, is the lack of clarity with which the machine itself can reason and the risk that this can pose. However, although the genie is out of the lamp, we can try to position ourselves at the front and center of the decision-making process and help inform innovators, inventors, and data scientists.

Much of the machine learning model is based on teaching the machine how to learn and reason, drawing from a number of mathematical models. In order to understand the underlying AI technology, it is helpful to take a closer look at how AI models are structured.

Machine Learning Models: Recurrence and Convolution Transformers

Recurrence and convolution transformers are 2 important concepts in AI that have been widely used in machine learning models. Recurrence helps models remember what happened before, whereas convolution finds important patterns in data and transformers focus on understanding relationships between different parts of the input.

Recurrence

Think of recurrence as a memory that helps a model remember information from previous steps. It is useful when dealing with things that happen in a specific order or over time. For example, if you are predicting the next word in a sentence, recurrence helps the model understand the words that came before it. It is like connecting the dots by looking at what happened before to make sense of what comes next.

Convolution

Convolution is like a filter that helps the model find important patterns in data. It is commonly used for tasks involving images or grids of data. Just like our brain focuses on specific parts of an image to understand it, convolution helps the model focus on important details. It looks for features like edges, shapes, and textures, allowing the model to recognize objects or understand the structure of the data.

Transformers

Transformers are like smart attention machines. They excel in understanding relationships between different parts of a sentence or data without needing to process them in order. They can find connections between words that are far apart from each other. Transformers are especially powerful in tasks like language translation, where understanding the context of each word is crucial. They work by paying attention to different words and weighing their importance based on their relationships.

How Transformers Became So Impactful

A landmark 2017 paper on AI titled, Attention Is All You Need by Vaswani and colleagues6 laid important work in understanding the transformer model. Unlike recurrence and convolution, the transformer model relies heavily on the self-attention mechanism. Self-attention allows the model to focus on different parts of the input sequence during processing, enabling it to capture long-range dependencies effectively. Attention mechanisms allow the model-to-model dependencies between input and output sequences without considering their distance. This allows the machine incredible advanced capabilities, especially when powered with advanced computing power.

Machine Learning Frameworks

Currently, there are several frameworks that can be applied to the machine learning process:

The CRISP-DM approach involves about 8 phases:

Concerns With AI

In medicine and psychiatry, we are familiar with distortions that can arise in human thinking. We know that thinking about what we are thinking about becomes an important skill in training the mind. In AI, the loss of human control and input in informing the machines is at the heart of many concerns. There are several reasons for this.

Addressing these concerns requires a comprehensive approach that emphasizes transparency, accountability, fairness, and human oversight in the development and deployment of AI systems. It is crucial to consider the societal impact of AI and to establish regulations and guidelines that ensure its responsible and ethical use.

Positives and Negatives in the Medical Community

For the medical community specifically, this new technology brings both positives and negatives. By leveraging the potential of AI while addressing its limitations and concerns, health care can benefit from improved diagnostics.

Positive aspects:

Negative aspects:

Evaluating AI Technology

A proposed mechanism for physicians and health care workers to evaluate technology might be a framework similar to what we have identified as an evidence-based tool. Here are some guiding questions for evaluating the technology:

A couple of suggested evaluation tools that can be used in interpreting AI models in health care are listed in Figures 1 and 2. These mnemonics can serve as a framework for health care professionals to systematically evaluate and interpret AI models, ensuring that ethical considerations, transparency, and accuracy are prioritized in the implementation and use of AI in health care.

Dr Amaladoss is a clinical assistant professor in the Department of Psychiatry and Behavioral Neurosciences at McMaster University. He is a clinicianscientistand educator who has been a recipientof anumberof teaching awards. His current research involves personalized medicine and theintersection of medicine and emerging technologies including developing machine learning models and AI in improving health care. Dr Amaladoss has also been involved with the recent task force on AI and emerging digital technologies at the Royal College of Physicians and Surgeons.

Dr Ahmed is an internal medicine resident at the University of Toronto. He has led and published research projects in multiple domains including evidence-based medicine, medical education, and cardiology.

References

1. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence.Nat Med. 2019;25(1):44-56.

2. Szolovits P. Ed. Artificial Intelligence in Medicine. Routledge; 1982.

3. London AJ. Artificial intelligence in medicine: overcoming or recapitulating structural challenges to improving patient care?Cell Rep Med. 2022;3(5):100622.

4. Larentzakis A, Lygeros N. Artificial intelligence (AI) in medicine as a strategic valuable tool.Pan Afr Med J. 2021;38:184.

5. Mohammad L, Jarenwattananon P, Summers J. An open letter signed by tech leaders, researchers proposes delaying AI development. NPR. March 29, 2023. Accessed August 1, 2023. https://www.npr.org/2023/03/29/1166891536/an-open-letter-signed-by-tech-leaders-researchers-proposes-delaying-ai-developme

6. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need. NIPS. June 12, 2017. Accessed August 10, 2023. https://www.semanticscholar.org/paper/Attention-is-All-you-Need-Vaswani-Shazeer/204e3073870fae3d05bcbc2f6a8e263d9b72e776

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Attention to Attention is What You Need: Artificial Intelligence and ... - Psychiatric Times

WASHINGTON, D.C. Today, the Consumer Financial Protection Bureau (CFPB) issued guidance about certain legal requirements that lenders must adhere to when using artificial intelligence and other complex models. The guidance describes how lenders must use specific and accurate reasons when taking adverse actions against consumers. This means that creditors cannot simply use CFPB sample adverse action forms and checklists if they do not reflect the actual reason for the denial of credit or a change of credit conditions. This requirement is especially important with the growth of advanced algorithms and personal consumer data in credit underwriting. Explaining the reasons for adverse actions help improve consumers chances for future credit, and protect consumers from illegal discrimination.

Technology marketed as artificial intelligence is expanding the data used for lending decisions, and also growing the list of potential reasons for why credit is denied, said CFPB Director Rohit Chopra. Creditors must be able to specifically explain their reasons for denial. There is no special exemption for artificial intelligence.

In todays marketplace, creditors are increasingly using complex algorithms, marketed as artificial intelligence, and other predictive decision-making technologies in their underwriting models. Creditors often feed these complex algorithms with large datasets, sometimes including data that may be harvested from consumer surveillance. As a result, a consumer may be denied credit for reasons they may not consider particularly relevant to their finances. Despite the potentially expansive list of reasons for adverse credit actions, some creditors may inappropriately rely on a checklist of reasons provided in CFPB sample forms. However, the Equal Credit Opportunity Act does not allow creditors to simply conduct check-the-box exercises when delivering notices of adverse action if doing so fails to accurately inform consumers why adverse actions were taken.

In fact, the CFPB confirmed in a circular from last year that the Equal Credit Opportunity Act requires creditors to explain the specific reasons for taking adverse actions. This requirement remains even if those companies use complex algorithms and black-box credit models that make it difficult to identify those reasons. Todays guidance expands on last years circular by explaining that sample adverse action checklists should not be considered exhaustive, nor do they automatically cover a creditors legal requirements.

Specifically, todays guidance explains that even for adverse decisions made by complex algorithms, creditors must provide accurate and specific reasons. Generally, creditors cannot state the reasons for adverse actions by pointing to a broad bucket. For instance, if a creditor decides to lower the limit on a consumers credit line based on behavioral spending data, the explanation would likely need to provide more details about the specific negative behaviors that led to the reduction beyond a general reason like purchasing history.

Creditors that simply select the closest factors from the checklist of sample reasons are not in compliance with the law if those reasons do not sufficiently reflect the actual reason for the action taken. Creditors must disclose the specific reasons, even if consumers may be surprised, upset, or angered to learn their credit applications were being graded on data that may not intuitively relate to their finances.

In addition to todays and last years circulars, the CFPB has issued an advisory opinion that consumer financial protection law requires lenders to provide adverse action notices to borrowers when changes are made to their existing credit.

The CFPB has made the intersection of fair lending and technology a priority. For instance, as the demand for digital, algorithmic scoring of prospective tenants has increased among corporate landlords, the CFPB reminded landlords that prospective tenants must receive adverse action notices when denied housing. The CFPB also has joined with other federal agencies to issue a proposed rule on automated valuation models, and is actively working to ensure that black-box models do not lead to acts of digital redlining in the mortgage market.

Read Consumer Financial Protection Circular 2023-03, Adverse action notification requirements and the proper use of the CFPBs sample forms provided in Regulation B.

Consumers can submit complaints about financial products and services by visiting the CFPBs website or by calling (855) 411-CFPB (2372).

Employees who believe their companies have violated federal consumer financial protection laws are encouraged to send information about what they know to whistleblower@cfpb.gov. Workers in technical fields, including those that design, develop, and implement artificial intelligence, may also report potential misconduct to the CFPB. To learn more, visit the CFPBs website.

The Consumer Financial Protection Bureau is a 21st century agency that implements and enforces Federal consumer financial law and ensures that markets for consumer financial products are fair, transparent, and competitive. For more information, visit consumerfinance.gov.

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CFPB Issues Guidance on Credit Denials by Lenders Using Artificial ... - Consumer Financial Protection Bureau

NYU President Linda G. Mills and Korean Advanced Institute of Science and Technology (KAIST) President Kwang Hyung Lee were joined by Sung Bae Jun, president of the Institute of Information & Communications Technology Planning & Evaluation, and Joon Hee Joh, president of the Korea Software Industry Association in signing an agreement to collaborate on a major Artificial Intelligence (AI) and digital technologies research effort.

Senior public officialsincluding the President of the Republic of Korea, Yoon Suk Yeol; Koreas Minister of Science and Information and Communications Technology Jong-Ho Lee; the Director of the US National Science Foundation Sethuraman Panchanathan; NYC Deputy Mayor for Housing, Economic Development, and Workforce Maria Torres-Springerand Turing Prize-winning AI scientist and NYU faculty member Yann LeCun convened at NYUs Greenwich Village campus to mark the new partnership and launch a Digital Vision Forum with leading thinkers on AI and digital governance from around the world. Senator Charles Schumer participated in the proceedings via video. The event also importantly marked the anniversary of the first Digital Vision Forum, which was held precisely a year ago at NYU to initiate the partnership between NYU, the Republic of Korea, and KAIST, an event that also featured remarks by President Yoon.

Todays historic event positions NYU, New York City, and Korea at the forefront of the global science and tech ecosystem of the future. NYU President Mills said, We are honored to bring together leaders in government, academia, and industry to commemorate a vital and historic partnership that will propel scholarship and advancements in technology. We are thrilled by this partnership, which exemplifies both NYUs commitment to global learning and research as well as our role in fueling the growth of New York Citys tech, science, and innovation sector.

Senator Schumer said, I want to commend President Yoon and my friend, NYU President Linda Mills, on todays announcement of a historic joint research program between NYU and the South Korean government. The partnership is a partnership made in heaven: NYU, one of the nations leading research institutions, and South Korea, one of Americas strongest allies and partners, and also a leader in research and science, collaborating on one of the most important issues of our time, artificial intelligence.

NSF Director Panchanathan said, As our two presidents affirmed at the State Visit in April, the U.S. and the Republic of Korea have a truly global alliance that champions democratic principles, enriches economic cooperation, and empowers technological advances. NSF shares in President Yoon's conviction that human values are important in the development of new technology. Values including openness and transparency, and the creation of AI tools that are responsible and ethical, without bias, and protect the security and privacy of our people.

The research effortthe ROK Institutions-NYU AI and Digital Partnershipaims to conduct world-class research in AI and digital technologies. The partnership is expected to be headquartered at NYU.

Todays event marks the expansion of NYUs previously announced partnership and strengthens the Universitys links to Korea and its institutions. The event included a wide-ranging panel discussion about AI and digital governance by prominent scholars in the field. The panel was moderated by Professor Matthew Liao, director of the Center for Bioethics at NYUs School of Global Public Health, the panelists included:

Professor Kyung-hyun Cho, Deputy Director for NYU Center for Data Science & Courant Institute

Professor Luciano Floridi, Founding Director of the Digital Ethics Center, Yale University

Professor Urs Gasser, Rector of the Hochschule fur Politik, Technical University of Munich

Professor Shannon Vallor, Baillie Gifford Professor in the Ethics of Data and Artificial Intelligence, University of Edinburgh

Professor Stefaan Verhulst, Co-Founder & Director of GovLab's Data Program, NYU Tandon School of Engineering, and

Professor Jong Chul Ye, Director of Promotion Council for Digital Health, KAIST

For NYUs new president, Linda G. Mills, this is the third major global agreement she has signed this month. She earlier signed a research partnership agreement with IIT Kanpur in India (an agreement cited by US President Joe Biden and Indian Prime Minister Narendra Modi in their joint statement) and renewed a partnership agreement between NYUs Shanghai campus and East China Normal University. Building on NYUs unrivaled global presence, strength, and character is expected to be a major priority of her administration.

About NYU

Founded in 1831, NYU is one of the worlds foremost research universities (with more than $1 billion per year in research expenditures) and is a member of the selective Association of American Universities. NYU has degree-granting university campuses in New York, Abu Dhabi, and Shanghai; has 12 other global academic sites, including London, Paris, Florence, Tel Aviv, Buenos Aires, and Accra; and both sends more students to study abroad and educates more international students than any other U.S. college or university. Through its numerous schools and colleges, NYU is a leader in conducting research and providing education in the arts and sciences, law, medicine, engineering, business, dentistry, education, nursing, the cinematic and performing arts, music and studio arts, public administration, social work, and professional studies, among other areas.

About KAIST

Since KAIST was established in 1971, KAIST and its alumni have been the gateway to advanced science and technology, innovation, and entrepreneurship and have made a significant contribution to creating the dynamic economy of todays Korea. KAIST has now emerged as one of the most innovative universities; it ranked 1st among the Most Innovative Universities in Asia from 2016 to 2018 and 11th in the Worlds Most Innovative Universities in 2018 by Thomson Reuters. KAIST was named as one of the Global 100 Innovators in 2021 by Clarivate, the only university listed. QS ranked KAIST the 20th-best university in engineering and technology in 2022, and the Nature Indexs Young University Ranking placed KAIST 4th in the world. KAIST continues to spearhead innovation and lead the advance of science and technology in Korea and beyond, and aims to contribute to the development of new dynamic engines of growth and innovation through collaboration with NYU to foster more future-oriented, creative global talents, young researchers, and entrepreneurs in the creative environment of New York City.

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NYU and KAIST Launch Major New Initiative on Artificial Intelligence ... - New York University

Porcha Woodruff was eight months pregnant when she was arrested for carjacking. The Detroit police used facial recognition technology to run an image of the carjacking suspect through a mugshot database, and Ms. Woodruffs photo was among those returned.

Ms. Woodruff, an aesthetician and nursing student who was preparing her two daughters for school, was shocked when officers told her that she was being arrested for a crime she did not commit. She was questioned over the course of 11 hours at the Detroit Detention Center.

A month later, the prosecutor dismissed the case against her based on insufficient evidence.

Ms. Woodruffs ordeal demonstrates the very real risk that cutting-edge artificial intelligence-based technology like the facial recognition software at issue in her case presents to innocent people, especially when such technology is neither rigorously tested nor regulated before it is deployed.

Time and again, facial recognition technology gets it wrong, as it did in Ms. Woodruffs case. Although its accuracy has improved over recent years, this technology still relies heavily on vast quantities of information that it is incapable of assessing for reliability. And, in many cases, that information is biased.

In 2016, Georgetown Universitys Center on Privacy & Technology noted that at least 26 states allow police officers to run or request to have facial recognition searches run against their drivers license and ID databases. Based on this figure, the center estimated that one in two American adults has their image stored in a law enforcement facial recognition network. Furthermore, given the disproportionate rate at which African Americans are subject to arrest, the center found that facial recognition systems that rely on mug shot databases are likely to include an equally disproportionate number of African Americans.

More disturbingly, facial recognition software is significantly less reliable for Black and Asian people, who, according to a study by the National Institute of Standards and Technology, were 10 to 100 times more likely to be misidentified than white people. The institute, along with other independent studies, found that the systems algorithms struggled to distinguish between facial structures and darker skin tones.

The use of such biased technology has had real-world consequences for innocent people throughout the country. To date, six people that we know of have reported being falsely accused of a crime following a facial recognition match all six were Black. Three of those who were falsely accused in Detroit have filed lawsuits, one of which urges the city to gather more evidence in cases involving facial recognition searches and to end the facial recognition to line-up pipeline.

Former Detroit Police Chief James Craig acknowledged that if the citys officers were to use facial recognition by itself, it would yield misidentifications 96% of the time.

Even when an AI-powered technology is properly tested, the risks of a wrongful arrest and wrongful conviction remain and are exacerbated by these new tools.

Thats because when AI identifies a suspect, it can create a powerful, unconscious bias against the technology-identified person that hardens the focus of an investigation away from other suspects.

Indeed, such technology-induced tunnel vision has already had damaging ramifications.

For example, in 2021, Michael Williams was jailed in Chicago for the first-degree murder of Safarian Herring based on a ShotSpotter alert that police received. Although ShotSpotter purports to triangulate a gunshots location through an AI algorithm and a network of microphones, an investigation by the Associated Press found that the system is deeply statistically unreliable because it can frequently miss live gunfire or mistake other sounds for gunshots. Still, based on the alert and a noiseless security video that showed a car driving through an intersection, Mr. Williams was arrested and jailed for nearly a year even though police and prosecutors never established a motive explaining his alleged involvement, had no witnesses to the murder, and found no physical evidence tying him to the crime. According to a federal lawsuit later filed by Mr. Williams, investigators also ignored other leads, including reports that another person had previously attempted to shoot Mr. Herring. Mr. Williams spent nearly a year in jail before the case against him was dismissed.

Cases like Ms. Woodruffs and Mr. Williams highlight the dangers of law enforcements overreliance on AI technology, including an unfounded belief that such technology is a fair and objective processor of data.

Absent comprehensive testing or oversight, the introduction of additional AI-driven technology will only increase the risk of wrongful conviction and may displace the effective policing strategies, such as community engagement and relationship-building, that we know can reduce wrongful arrests.

We enter this fall with a number of significant victories under our belt including 7 exonerations since the start of the year.Through the cases of people like Rosa Jimenez and Leonard Mack, weve leveraged significant advances in DNA technology and other sciences to free innocent people from prison.

We are committed to countering the harmful effects of emerging technologies, advocating for research on AIs reliability and validity, and urging consideration of the ethical, legal, social and racial justice implications of its use.

We support a moratorium on the use of facial recognition technology in the criminal legal system until such time as research establishes its validity and impacted communities are given the opportunity to weigh in on the scope of its implementation.

We are pushing for more transparency around so-called black box technologies technologies whose inner workings are hidden from users.

We believe that any law enforcement reliance on AI technology in a criminal case must be immediately disclosed to the defense and subjected to rigorous adversarial testing in the courtroom.

Building on President Bidens executive order directing the National Academy of Sciences to study certain AI-based technologies that can lead to wrongful convictions, we are also collaborating with various partners to collect the necessary data to enact reforms.

And, finally, we encourage Congress to make explicit the ways in which it will regulate investigative technologies to protect personal data.

It is only through these efforts can we protect innocent people from further risk of wrongful conviction in todays digital age.

With gratitude,

Christina SwarnsExecutive Director, Innocence Project

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When Artificial Intelligence Gets It Wrong - Innocence Project

The rapid growth of artificial intelligence (AI) development and adoption, particularly generative AI and machine learning applications, has captured the attention of business leaders, academics, investors, and regulators worldwide. AI is also requiring companies to confront an evolving host of questions across different areas of law, including privacy, cybersecurity, commercial and intellectual property transactions, intellectual property ownership and rights, products liability, labor and employment, insurance, consumer protection, corporate governance, national security, ethics, government policy, and regulation.

Below, we outline questions that companies and their boards should consider as they navigate this ever-evolving technological innovation. Many of these questions are industry-agnostic, but all companies must also address challenges specific to the industry and regulatory environment in which they operate.

Sidley has a multi-disciplinary AI industry team focused on providing our clients with practical and actionable guidance on the wide range of regulatory, transactional, and litigation issues companies face in evaluating, leveraging, and mitigating risk from AI.

To discuss the business and legal implications for your company, please contact one of the individuals below or one of the dedicated Sidley lawyers with whom you work.

Updated: September 19, 2023

In light of the evolving situation, we are reviewing and frequently updating information provided in the PDF.

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Artificial Intelligence: Key Business and Legal Issues to Consider - Sidley Austin LLP