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Found 14 papers, 6 papers with code
Distinguishing the Indistinguishable: Human Expertise in Algorithmic Prediction
Our approach focuses on the use of human judgment to distinguish inputs which `look the same' to any feasible predictive algorithm.
Reconciling the accuracy-diversity trade-off in recommendations
In recommendation settings, there is an apparent trade-off between the goals of accuracy (to recommend items a user is most likely to want) and diversity (to recommend items representing a range of categories).
Auditing for Human Expertise
A rejection of our test thus suggests that human experts may add value to any algorithm trained on the available data, and has direct implications for whether human-AI `complementarity' is achievable in a given prediction task.
Simplistic Collection and Labeling Practices Limit the Utility of Benchmark Datasets for Twitter Bot Detection
These tools employ machine learning and often achieve near perfect performance for classification on existing datasets, suggesting bot detection is accurate, reliable and fit for use in downstream applications.
Fairness On The Ground: Applying Algorithmic Fairness Approaches to Production Systems
We discuss how we disentangle normative questions of product and policy design (like, "how should the system trade off between different stakeholders' interests and needs?")
Algorithmic Monoculture and Social Welfare
Here we show that the dangers of algorithmic monoculture run much deeper, in that monocultural convergence on a single algorithm by a group of decision-making agents, even when the algorithm is more accurate for any one agent in isolation, can reduce the overall quality of the decisions being made by the full collection of agents.
Bridging Machine Learning and Mechanism Design towards Algorithmic Fairness
Decision-making systems increasingly orchestrate our world: how to intervene on the algorithmic components to build fair and equitable systems is therefore a question of utmost importance; one that is substantially complicated by the context-dependent nature of fairness and discrimination.
Greedy Algorithm almost Dominates in Smoothed Contextual Bandits
Online learning algorithms, widely used to power search and content optimization on the web, must balance exploration and exploitation, potentially sacrificing the experience of current users in order to gain information that will lead to better decisions in the future.
Mitigating Bias in Algorithmic Hiring: Evaluating Claims and Practices
How are algorithmic assessments built, validated, and examined for bias?
How Do Classifiers Induce Agents To Invest Effort Strategically?
Algorithms are often used to produce decision-making rules that classify or evaluate individuals.
The Externalities of Exploration and How Data Diversity Helps Exploitation
Returning to group-level effects, we show that under the same conditions, negative group externalities essentially vanish under the greedy algorithm.
Selection Problems in the Presence of Implicit Bias
Over the past two decades, the notion of implicit bias has come to serve as an important component in our understanding of discrimination in activities such as hiring, promotion, and school admissions.
On Fairness and Calibration
The machine learning community has become increasingly concerned with the potential for bias and discrimination in predictive models.
Inherent Trade-Offs in the Fair Determination of Risk Scores
Recent discussion in the public sphere about algorithmic classification has involved tension between competing notions of what it means for a probabilistic classification to be fair to different groups.
