May 16th, 2023,
9:00am – 5:00pm
Johnson Rooms, 3rd Floor
Lurie Engineering Center
2023 Ethical AI Forum
From Theory to Practice: Building Ethical and Trustworthy AI
Event recordings forthcoming
Image credit: DALL-E
Overview
From Theory to Practice: Building Ethical and Trustworthy AI
About: Every day, whether we realize it or not, we are constantly surrounded by AI technology. From self-driving cars, to facial recognition software, fraud prevention models, recommender systems, ChatGPT, etc., AI is rapidly transforming our lives. But do we fully comprehend the real range of potential ethical implications related to its use and regulation? This event will stimulate ideas and investigation into that question by bringing together academics, leaders and scientists in the private sector and policy regulation areas, to share their knowledge and discuss ethical challenges and trends in AI regulation, along with cutting-edge theory and implementation of ethical and transparent AI models. The event is free and open to all who develop AI methods, are current or future users of AI, or are curious about how AI will shape research and our society.
Organizers: as a facilitator of the development and application of data science (DS) and AI techniques for the broad U-M data science community, MIDAS is also imbued with the mission of promoting ethical research. In fact, one of the five research pillars that MIDAS supports is ‘Responsible Research’, focused on enhancing the scientific and societal impact of DS and AI, which takes place especially through fomenting the discussion and expansion of the Ethical AI field. On the other hand, as a prominent player in the private sector, Rocket Companies constantly strive for learning and applying responsible cutting-edge tools in AI. Joined with a common interest in the Ethical AI field, MIDAS and Rocket Companies are inviting you to share your views and learn together about breakthroughs and pressing issues regarding ethical AI.
Post-event Summary: MIDAS hosts forum on ethics in artificial intelligence (the Michigan Daily)
Schedule
Forum Schedule
May 16th, 2023,
9:00am – 5:00pm
Johnson Rooms, 3rd Floor
Lurie Engineering Center
8:40 AM: Coffee Available
9:00 AM: Opening Remarks
H.V.Jagadish, Director, Michigan Institute for Data Science; Edgar F Codd Distinguished University Professor and Bernard A Galler Collegiate Professor of Electrical Engineering and Computer Science
9:05 AM: Ethical AI at Rocket Companies
Brian Stucky, Team Lead, Rocket Ethical AI
Trevor Ferry, Senior Product Owner, Rocket Ethical AI
Lucia Wang, Data Scientist, Rocket Ethical AI
Ameya Diwan, Ethical AI Analyst, Rocket Ethical AI
9:40 AM: Academic Lightning Talks – Round 1
Systemic algorithmic harms in the mortgage industryLu Xian, Ph.D. Student, School of Information, University of Michigan We document how and when algorithms interact with, and amplify, the unequal aspects of the mortgage industry. By documenting the systemic harms, we highlight the harmful impacts of the seemingly-positive expansion of opportunity and inclusion of minority communities, which the adoption of algorithmic decision-making systems promises. This analysis expands the scope of focus from the individual-based harms of an application denial to a broader, community-wide, and sociohistorical conception of harm. We call for making legible the ways algorithms reinforce and exacerbate injustices, and we urge future interventions to acutely account for context and community-based harms moving forward. We provide lessons drawn from the mortgage industry for identifying and addressing systemic algorithmic harms. |
Trustworthiness and Explainable AI: Perspectives from Advanced ManufacturingJoseph (Yossi) Cohen, Schmidt AI in Science Fellow, Michigan Institute for Data Science (MIDAS), University of Michigan |
Detecting and Countering Untrustworthy Artificial Intelligence (AI)Nikola Banovic, Assistant Professor, Electrical Engineering and Computer Science, University of Michigan |
Development of Understandable Artificial Intelligence (UAI) Methods in Physical SciencesY Z, Professor, Nuclear Engineering and Radiological Sciences, University of Michigan |
From Extraction to Empowerment: Recent developments in Community-Based Computing InfrastructuresKwame Porter Robinson, Ph.D. Student, School of Information, University of Michigan Funded by the National Science Foundation (NSF), we examine how participatory experiments involving AI, digital fabrication and other techniques can build CBIs from the bottom-up, starting with locally owned artisanal enterprises. These experiments align with the literature on solidarity economies and AI ethics by incorporating ethical computation among physical fabrication and transportation concerns. Here we utilize two approaches at three scales. At the micro-scale, digital fabrication is used to enhance product variety and eliminate tedious aspects of artisanal labor, allowing more time and focus on creativity for artisanal business — such as textiles, urban farming and beauty salons. At the meso-scale, we create intervening technologies on goods delivery using routing algorithms, deliberative consumption as ways of reconnecting people and production. Our initial work involves multiplex routing for economic, social, and environmental sustainability through a goods delivery application in a Detroit network of producers and consumers. At the macro scale, our experiments integrate into a platform called Artisanal Futures, that promotes participatory AI development within businesses and their communities. This platform can be thought of as another possible infrastructure, a computational modernization of older Indigenous traditions, often formalized by Ostrom’s work on common-pool resource management. Through CBIs, we aim to promote social, economic, and environmental empowerment that benefit both people and the planet. |
11:00 AM: Coffee Break
11:15 AM: Keynote #1 – Recognizing and Eliminating Harmful Biases in AI for Healthcare
Jenna Wiens, Associate Professor, Computer Science and Engineering, University of Michigan
Jenna Wiens is an Associate Professor of Computer Science and Engineering (CSE), Associate Director of the Artificial Intelligence Lab, and co-Director of Precision Health at the University of Michigan in Ann Arbor. Her primary research interests lie at the intersection of machine learning and healthcare. Wiens received her PhD from MIT in 2014, and her notable achievements include an NSF CAREER award in 2016, being named as an Innovator Under 35 by the MIT Tech Review in 2017, and receiving a Sloan Research Fellowship in Computer Science.
12:05 PM: Panel Discussion – Different Perspectives: Academia and Practice
- Moderator: David Corliss, AVP, Data Science, OnStar Insurance
- Jenna Wiens, Associate Professor, Computer Science and Engineering, University of Michigan
- Brian Stucky, Team Lead, Rocket Ethical AI
12:40 PM: Lunch Break
1:30 PM: Keynote #2 – Evaluation and Values in Machine Learning and NLP
Dallas Card, Assistant Professor, School of Information, University of Michigan
Dallas Card is an Assistant Professor in the School of Information at the University of Michigan, where his research focuses on making machine learning more reliable and responsible, and on using machine learning and natural language processing to learn about society from text. His work received a best short paper nomination at ACL 2019, a distinguished paper award at FAccT 2022, and has been covered in Vox, Wired, and other outlets. Prior to starting at Michigan, Dallas was a postdoctoral researcher with the Stanford Natural Language Processing Group and the Stanford Data Science Institute, and received his Ph.D. in Machine Learning from Carnegie Mellon University.
2:20 PM: Academic Lightning Talks – Round 2
On the Interaction between Robustness and Fairness in Machine LearningHan Xu, Ph.D. Student, Department of Computer Science and Engineering, Michigan State University |
Design Fiction or Design Engineering? A Speculative Sandbox for Ethical Decision-MakingElisa Ngan, Assistant Professor of Practice, Urban Technology, Taubman College of Architecture and Urban Planning, University of Michigan |
Should Privacy Rights Constrain Machine Inference? Can They?Cameron McCulloch, Ph.D. Student, Department of Philosophy, University of Michigan This paper asks a similar question about inferences made by machine learning algorithms: If an initial set of data, D, is acquired justly (whether by a corporation, state, or individual actor), are there any inferences from D that are illegitimate? Widely shared moral intuitions suggest the answer must be “Yes.” In particular, it is often suggested that privacy rights ought to constrain what sorts of inferences companies are licensed to make about individuals on the basis of machine learning. (Consider cases like the much-discussed Target “pregnancy case,” in which a young woman was outed as pregnant to her family by marketing materials Target sent her on the basis of knowing she was pregnant, a fact the young woman never shared with Target.) There are a variety of reasons people have gestured at which seem to draw a distinction between ordinary human inference and machine inference, reasons that supposedly ground a moral limitation on machine inference—unfair inferential power, the use of illegitimate statistical generalizations, and more. But it’s surprisingly hard to come up with an articulate constraint that is both morally non-arbitrary and practically tenable. In this “problem paper,” I present five reasons why it is difficult to come up with a principled constraint on machine inference that does not also impinge on individual cognitive liberty. The difficulties are both theoretical (difficulty skirting moral arbitrariness) and practical (difficulty shaping unsupervised deep learning networks). |
3:10 PM: Coffee Break
3:25 PM: Keynote #3 – AI Policy in US and EU
Merve Hickok, President, Center for AI & Digital Policy
Merve Hickok is the President and Research Director at Center for AI & Digital Policy. The Center educates AI policy practitioners and advocates across 60+ countries and leads a research group which advises international organizations (such as European Commission, UNESCO, the Council of Europe, OECD, etc) on AI policy and regulatory developments. She is also a Data Ethics Lecturer at University of Michigan, School of Information; and the founder of AIethicist.org. She is a researcher, trainer, and consultant working on AI policy, governance and regulation. She focuses on AI bias, impact of AI systems on fundamental rights, democratic values, and social justice. She provides consultancy and training services to private & public organizations on Responsible AI – ethical and responsible development, use and governance of AI.
Merve also works with several non-profit organizations globally to advance both the academic and professional research in this field for underrepresented groups. She has been recognized by a number of organizations – most recently as one of the 100 Brilliant Women in AI Ethics™ – 2021, and as Runner-up for Responsible AI Leader of the Year – 2022 (Women in AI).
4:15 PM: Panel Discussion – Different Perspectives: Academia & Practice
- Moderators: Michigan Data Science Fellows Elyse Thulin, efrén cruz cortés, and Bernardo Modenesi
- Dallas Card, Assistant Professor, School of Information, University of Michigan
- Merve Hickok, President, Center for AI & Digital Policy
4:50 PM: Q&A Session and Final Remarks
About the Organizer
Data Science Fellow
Michigan Institute for Data Science
About: the labor market is a setting increasingly disrupted by AI (both in allocation of wages and of jobs) and yet understudied in the ethical AI research space. My research agenda has been focused on the combination of unsupervised learning methods, from network theory, and discrete choice tools, in order to improve the understanding of labor market dynamics and consequently promote evidence for oversight and regulation towards labor market fairness.
AI also shapes the lives of households through mortgages. I have been also interested in exploring interpretability and fairness questions related to AI automated decisions in the mortgage industry, in partnership with the Rocket Companies. In addition to topics related to the nature of the mortgage decision algorithms, I also plan to explore the impact of mortgage decisions in opportunities in life.
Data Science Fellow
Michigan Institute for Data Science
About: efrén studies the way algorithms reproduce bias and discrimination. Automated procedures are often designed to mimic the historical data humans have generated. Therefore, unintendedly, they have learned to discriminate based on class, race, gender, and other vulnerable groups. Such a phenomenon has serious consequences, as it may lead to furthering economic inequality, depriving the poor of resources, over-incarceration of people of color, etc. efrén’s goal is to understand the dynamics of the system the algorithm belongs to and assess which structural interventions are the best actions to both avoid discrimination and accomplish the desired goal for the population of interest.
Contact Us
Questions?
Message the MIDAS team: midas-contact@umich.edu