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Found 26 papers, 4 papers with code
Red-Teaming for Generative AI: Silver Bullet or Security Theater?
In response to rising concerns surrounding the safety, security, and trustworthiness of Generative AI (GenAI) models, practitioners and regulators alike have pointed to AI red-teaming as a key component of their strategies for identifying and mitigating these risks.
Assessing AI Impact Assessments: A Classroom Study
Artificial Intelligence Impact Assessments ("AIIAs"), a family of tools that provide structured processes to imagine the possible impacts of a proposed AI system, have become an increasingly popular proposal to govern AI systems.
RELand: Risk Estimation of Landmines via Interpretable Invariant Risk Minimization
Landmines remain a threat to war-affected communities for years after conflicts have ended, partly due to the laborious nature of demining tasks.
The AI Incident Database as an Educational Tool to Raise Awareness of AI Harms: A Classroom Exploration of Efficacy, Limitations, & Future Improvements
Prior work has established the importance of integrating AI ethics topics into computer and data sciences curricula.
Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools
While algorithmic fairness is a thriving area of research, in practice, mitigating issues of bias often gets reduced to enforcing an arbitrarily chosen fairness metric, either by enforcing fairness constraints during the optimization step, post-processing model outputs, or by manipulating the training data.
Fine-Tuning Games: Bargaining and Adaptation for General-Purpose Models
Both entities are profit-seeking and incur costs when they invest in the technology, and they must reach a bargaining agreement on how to share the revenue for the technology to reach the market.
Beneficent Intelligence: A Capability Approach to Modeling Benefit, Assistance, and Associated Moral Failures through AI Systems
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.
Moral Machine or Tyranny of the Majority?
With Artificial Intelligence systems increasingly applied in consequential domains, researchers have begun to ask how these systems ought to act in ethically charged situations where even humans lack consensus.
Recentering Validity Considerations through Early-Stage Deliberations Around AI and Policy Design
AI-based decision-making tools are rapidly spreading across a range of real-world, complex domains like healthcare, criminal justice, and child welfare.
Informational Diversity and Affinity Bias in Team Growth Dynamics
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 that do not.
A Validity Perspective on Evaluating the Justified Use of Data-driven Decision-making Algorithms
Recent research increasingly brings to question the appropriateness of using predictive tools in complex, real-world tasks.
Perspectives on Incorporating Expert Feedback into Model Updates
A practitioner may receive feedback from an expert at the observation- or domain-level, and convert this feedback into updates to the dataset, loss function, or parameter space.
A Taxonomy of Human and ML Strengths in Decision-Making to Investigate Human-ML Complementarity
To illustrate how our taxonomy can be used to investigate complementarity, we provide a mathematical aggregation framework to examine enabling conditions for complementarity.
A Sandbox Tool to Bias(Stress)-Test Fairness Algorithms
This stylized setup offers the distinct capability of testing fairness interventions beyond observational data and against an unbiased benchmark.
Bayesian Persuasion for Algorithmic Recourse
While the decision maker's problem of finding the optimal Bayesian incentive-compatible (BIC) signaling policy takes the form of optimization over infinitely-many variables, we show that this optimization can be cast as a linear program over finitely-many regions of the space of possible assessment rules.
Strategic Instrumental Variable Regression: Recovering Causal Relationships From Strategic Responses
In settings where Machine Learning (ML) algorithms automate or inform consequential decisions about people, individual decision subjects are often incentivized to strategically modify their observable attributes to receive more favorable predictions.
Stateful Strategic Regression
In particular, we consider settings in which the agent's effort investment today can accumulate over time in the form of an internal state - impacting both his future rewards and that of the principal.
Addressing the Long-term Impact of ML Decisions via Policy Regret
To capture the long-term effects of ML-based allocation decisions, we study a setting in which the reward from each arm evolves every time the decision-maker pulls that arm.
Allocating Opportunities in a Dynamic Model of Intergenerational Mobility
We develop a dynamic model for allocating such opportunities in a society that exhibits bottlenecks in mobility; the problem of optimal allocation reflects a trade-off between the benefits conferred by the opportunities in the current generation and the potential to elevate the socioeconomic status of recipients, shaping the composition of future generations in ways that can benefit further from the opportunities.
Computers and Society Physics and Society
A Human-in-the-loop Framework to Construct Context-aware Mathematical Notions of Outcome Fairness
Our family of fairness notions corresponds to a new interpretation of economic models of Equality of Opportunity (EOP), and it includes most existing notions of fairness as special cases.
On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning
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 of the algorithmic decisions today on the long-term welfare and prosperity of certain segments of the population.
A Moral Framework for Understanding of Fair ML through Economic Models of Equality of Opportunity
In this respect, our work serves as a unifying moral framework for understanding existing notions of algorithmic fairness.
A Unified Approach to Quantifying Algorithmic Unfairness: Measuring Individual & Group Unfairness via Inequality Indices
Further, our work reveals overlooked tradeoffs between different fairness notions: using our proposed measures, the overall individual-level unfairness of an algorithm can be decomposed into a between-group and a within-group component.
Fairness Behind a Veil of Ignorance: A Welfare Analysis for Automated Decision Making
We draw attention to an important, yet largely overlooked aspect of evaluating fairness for automated decision making systems---namely risk and welfare considerations.
A Convex Framework for Fair Regression
We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems.
Fairness in Criminal Justice Risk Assessments: The State of the Art
Methods: We draw on the existing literatures in criminology, computer science and statistics to provide an integrated examination of fairness and accuracy in criminal justice risk assessments.
