196 citations · 606 across the 27 of their papers we have counts for
30 papers
Modeling the Economic Impacts of AI Openness Regulation
Tori Qiu, Benjamin Laufer, Jon Kleinberg +1
Regulatory frameworks, such as the EU AI Act, encourage openness of general-purpose AI models by offering legal exemptions for "open-source" models. Despite this legislative attent…
Designing Algorithmic Delegates: The Role of Indistinguishability in Human-AI Handoff
Sophie Greenwood, Karen Levy, Solon Barocas +2
As AI technologies improve, people are increasingly willing to delegate tasks to AI agents. In many cases, the human decision-maker chooses whether to delegate to an AI agent based…
The Backfiring Effect of Weak AI Safety Regulation
Benjamin Laufer, Jon Kleinberg, Hoda Heidari
Recent policy proposals aim to improve the safety of general-purpose AI, but there is little understanding of the efficacy of different regulatory approaches to AI safety. We prese…
International AI Safety Report
Yoshua Bengio, Sören Mindermann, Daniel Privitera +93
The first International AI Safety Report comprehensively synthesizes the current evidence on the capabilities, risks, and safety of advanced AI systems. The report was mandated by…
International Scientific Report on the Safety of Advanced AI (Interim Report)
Yoshua Bengio, Sören Mindermann, Daniel Privitera +41
This is the interim publication of the first International Scientific Report on the Safety of Advanced AI. The report synthesises the scientific understanding of general-purpose AI…
Beneficent Intelligence: A Capability Approach to Modeling Benefit, Assistance, and Associated Moral Failures through AI Systems
Alex John London, Hoda Heidari
The prevailing discourse around AI ethics lacks the language and formalism necessary to capture the diverse ethical concerns that emerge when AI systems interact with individuals.…
Fine-Tuning Games: Bargaining and Adaptation for General-Purpose Models
Benjamin Laufer, Jon Kleinberg, Hoda Heidari
Recent advances in Machine Learning (ML) and Artificial Intelligence (AI) follow a familiar structure: A firm releases a large, pretrained model. It is designed to be adapted and t…
Moral Machine or Tyranny of the Majority?
Michael Feffer, Hoda Heidari, Zachary C. Lipton
With Artificial Intelligence systems increasingly applied in consequential domains, researchers have begun to ask how these systems ought to act in ethically charged situations whe…
Recentering Validity Considerations through Early-Stage Deliberations Around AI and Policy Design
Anna Kawakami, Amanda Coston, Haiyi Zhu +2
AI-based decision-making tools are rapidly spreading across a range of real-world, complex domains like healthcare, criminal justice, and child welfare. A growing body of research…
Informational Diversity and Affinity Bias in Team Growth Dynamics
Hoda Heidari, Solon Barocas, Jon Kleinberg +1
Prior work has provided strong evidence that, within organizational settings, teams that bring a diversity of information and perspectives to a task are more effective than teams t…
Four Years of FAccT: A Reflexive, Mixed-Methods Analysis of Research Contributions, Shortcomings, and Future Prospects
Benjamin Laufer, Sameer Jain, A. Feder Cooper +2
Fairness, Accountability, and Transparency (FAccT) for socio-technical systems has been a thriving area of research in recent years. An ACM conference bearing the same name has bee…
A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms
Amanda Coston, Anna Kawakami, Haiyi Zhu +2
Recent research increasingly brings to question the appropriateness of using predictive tools in complex, real-world tasks. While a growing body of work has explored ways to improv…
Perspectives on Incorporating Expert Feedback into Model Updates
Valerie Chen, Umang Bhatt, Hoda Heidari +2
Machine learning (ML) practitioners are increasingly tasked with developing models that are aligned with non-technical experts' values and goals. However, there has been insufficie…
A Sandbox Tool to Bias(Stress)-Test Fairness Algorithms
Nil-Jana Akpinar, Manish Nagireddy, Logan Stapleton +4
Motivated by the growing importance of reducing unfairness in ML predictions, Fair-ML researchers have presented an extensive suite of algorithmic 'fairness-enhancing' remedies. Mo…
Bayesian Persuasion for Algorithmic Recourse
Keegan Harris, Valerie Chen, Joon Sik Kim +3
When subjected to automated decision-making, decision subjects may strategically modify their observable features in ways they believe will maximize their chances of receiving a fa…
Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses
Keegan Harris, Daniel Ngo, Logan Stapleton +2
In settings where Machine Learning (ML) algorithms automate or inform consequential decisions about people, individual decision subjects are often incentivized to strategically mod…
Addressing the Long-term Impact of ML Decisions via Policy Regret
David Lindner, Hoda Heidari, Andreas Krause
Machine Learning (ML) increasingly informs the allocation of opportunities to individuals and communities in areas such as lending, education, employment, and beyond. Such decision…
Stateful Strategic Regression
Keegan Harris, Hoda Heidari, Zhiwei Steven Wu
Automated decision-making tools increasingly assess individuals to determine if they qualify for high-stakes opportunities. A recent line of research investigates how strategic age…
On Modeling Human Perceptions of Allocation Policies with Uncertain Outcomes
Hoda Heidari, Solon Barocas, Jon Kleinberg +1
Many policies allocate harms or benefits that are uncertain in nature: they produce distributions over the population in which individuals have different probabilities of incurring…
Allocating Opportunities in a Dynamic Model of Intergenerational Mobility
Hoda Heidari, Jon Kleinberg
Opportunities such as higher education can promote intergenerational mobility, leading individuals to achieve levels of socioeconomic status above that of their parents. We develop…
A Human-in-the-loop Framework to Construct Context-aware Mathematical Notions of Outcome Fairness
Mohammad Yaghini, Andreas Krause, Hoda Heidari
Existing mathematical notions of fairness fail to account for the context of decision-making. We argue that moral consideration of contextual factors is an inherently human task. S…
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning
Hoda Heidari, Vedant Nanda, Krishna P. Gummadi
Most existing notions of algorithmic fairness are one-shot: they ensure some form of allocative equality at the time of decision making, but do not account for the adverse impact o…
Mathematical Notions vs. Human Perception of Fairness: A Descriptive Approach to Fairness for Machine Learning
Megha Srivastava, Hoda Heidari, Andreas Krause
Fairness for Machine Learning has received considerable attention, recently. Various mathematical formulations of fairness have been proposed, and it has been shown that it is impo…
A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunity
Hoda Heidari, Michele Loi, Krishna P. Gummadi +1
We map the recently proposed notions of algorithmic fairness to economic models of Equality of opportunity (EOP)---an extensively studied ideal of fairness in political philosophy.…