counterfactual reasoning in algorithmic fairness
play

Counterfactual Reasoning in Algorithmic Fairness Ricardo Silva - PowerPoint PPT Presentation

Sep 09, 2023 •16 likes •686 views

Counterfactual Reasoning in Algorithmic Fairness Ricardo Silva University College London and The Alan Turing Institute Joint work with Matt Kusner (Warwick/Turing), Chris Russell (Sussex/Turing), and Joshua Loftus (NYU) Fairness and Machine

  1. Counterfactual Reasoning in Algorithmic Fairness Ricardo Silva University College London and The Alan Turing Institute Joint work with Matt Kusner (Warwick/Turing), Chris Russell (Sussex/Turing), and Joshua Loftus (NYU)

  2. Fairness and Machine Learning • The dream: if we teach machines to perform sensitive decisions, they will not suffer from human biases. • The reality: the GIGO principle still holds, regardless of whether we are talking of statistical models or software.

  3. The Message • There is only so much data alone can tell you about fairness. • I’m not talking about “just” value judgments . • We should highlight the role that the data- generating causal process has in shaping our notions of fairness.

  4. Nobody is Saying This is Easy • At no point I will suggest that building a causal model is easy. • Some untested and untestable assumptions will be needed. • The idea is to make your assumptions as explicit as possible, hopefully being “less wrong” in the end.

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Fair Classification with Counterfactual Learning Dr. Maryam Tavakol 1/15 What is Fairness 2/15

Fair Classification with Counterfactual Learning Dr. Maryam Tavakol 1/15 What is Fairness 2/15

July 2020 Fair Classification with Counterfactual Learning Dr. Maryam Tavakol 1/15 What is Fairness 2/15 ML/DM Basics Collecting the data (pre-processing, cleaning, etc.) Learning a model that fits the data (optimizing an objective) 3/15

447 views • 20 slides
Fairness, equality, and power in algorithmic decision making Rediet Abebe Maximilian Kasy May

Fairness, equality, and power in algorithmic decision making Rediet Abebe Maximilian Kasy May

Fairness, equality, and power in algorithmic decision making Rediet Abebe Maximilian Kasy May 2020 Introduction Public debate and the computer science literature: Fairness of algorithms, understood as the absence of discrimination . We

411 views • 26 slides
ETHICS & FAIRNESS IN AI- ETHICS & FAIRNESS IN AI- ENABLED SYSTEMS ENABLED SYSTEMS

ETHICS & FAIRNESS IN AI- ETHICS & FAIRNESS IN AI- ENABLED SYSTEMS ENABLED SYSTEMS

ETHICS & FAIRNESS IN AI- ETHICS & FAIRNESS IN AI- ENABLED SYSTEMS ENABLED SYSTEMS Christian Kaestner (with slides from Eunsuk Kang) Required reading: R. Caplan, J. Donovan, L. Hanson, J. Matthews. " Algorithmic Accountability:

1.41k views • 75 slides
Nixon, Light Switches and King Ludwig of Bavaria: How to Model Counterfactual Reasoning Katrin

Nixon, Light Switches and King Ludwig of Bavaria: How to Model Counterfactual Reasoning Katrin

Nixon, Light Switches and King Ludwig of Bavaria: How to Model Counterfactual Reasoning Katrin Schulz dinsdag, 27 september 2011 1 Conditionals between disciplines Conditionals Linguistics Institute for Logic, Language and Computation (ILLC)

979 views • 69 slides
Fairness and Transparency in Ranking Carlos Castillo / UPF chato@acm.org Data and Algorithmic

Fairness and Transparency in Ranking Carlos Castillo / UPF chato@acm.org Data and Algorithmic

Fairness and Transparency in Ranking Carlos Castillo / UPF chato@acm.org Data and Algorithmic Bias Workshop (DAB) at CIKM'18 Turin, Italy, 2018-10-22 Ranking in IR Objective : provide maximum relevance to searche r Order by decreasing

1.01k views • 62 slides
Inherent Trade-Offs in Algorithmic Fairness Instructor: Haifeng Xu COMPAS: A Risk Prediction T

Inherent Trade-Offs in Algorithmic Fairness Instructor: Haifeng Xu COMPAS: A Risk Prediction T

CS6501: T opics in Learning and Game Theory (Fall 2019) Inherent Trade-Offs in Algorithmic Fairness Instructor: Haifeng Xu COMPAS: A Risk Prediction T ool to Criminal Justice Correctional Offender Management Profiling for Alternative

1.03k views • 48 slides
COUNTERFACTUAL REASONING Jonathan Laurent , Jean Yang (Carnegie Mellon University), Walter Fontana

COUNTERFACTUAL REASONING Jonathan Laurent , Jean Yang (Carnegie Mellon University), Walter Fontana

CAUSAL ANALYSIS OF RULE-BASED MODELS THROUGH COUNTERFACTUAL REASONING Jonathan Laurent , Jean Yang (Carnegie Mellon University), Walter Fontana (Harvard Medical School) CAUSAL ANALYSIS Some techniques have been developed to analyze the causal

646 views • 33 slides
Recent Results on Algorithmic Fairness and Meta-Learning Massimiliano Pontil Computational

Recent Results on Algorithmic Fairness and Meta-Learning Massimiliano Pontil Computational

Recent Results on Algorithmic Fairness and Meta-Learning Massimiliano Pontil Computational Statistics and Machine Learning Istituto Italiano di Tecnologia and Department of Computer Science University College London 4th Annual Machine

439 views • 26 slides
AI Ethics for AI Practitioners A design framework for building towards algorithmic justice Willie

AI Ethics for AI Practitioners A design framework for building towards algorithmic justice Willie

AI Ethics for AI Practitioners A design framework for building towards algorithmic justice Willie Costello The hazards of algorithmic systems Bias Discrimination Racism Sexism Surveillance The ideals of algorithmic systems Fairness

575 views • 28 slides
INTRO TO ETHICS AND INTRO TO ETHICS AND FAIRNESS FAIRNESS Eunsuk Kang Required reading: R.

INTRO TO ETHICS AND INTRO TO ETHICS AND FAIRNESS FAIRNESS Eunsuk Kang Required reading: R.

INTRO TO ETHICS AND INTRO TO ETHICS AND FAIRNESS FAIRNESS Eunsuk Kang Required reading: R. Caplan, J. Donovan, L. Hanson, J. Matthews. "Algorithmic Accountability: A Primer", Data & Society (2018). 1 LEARNING GOALS LEARNING

732 views • 48 slides
Measuring social biases in human annotators using counterfactual queries in Crowdsourcing BHAVYA

Measuring social biases in human annotators using counterfactual queries in Crowdsourcing BHAVYA

Measuring social biases in human annotators using counterfactual queries in Crowdsourcing BHAVYA GHAI PhD Candidate, Computer Science Department Stony Brook University Adviser: Prof. Klaus Mueller Algorithmic Bias When Algorithms exhibit

291 views • 16 slides
Model-based reasoning DPLL(+ T ): algorithmic reasoner + first-order prover DPLL(+ T ) +

Model-based reasoning DPLL(+ T ): algorithmic reasoner + first-order prover DPLL(+ T ) +

Outline Model-based reasoning DPLL( + T ): algorithmic reasoner + first-order prover DPLL( + T ) + speculative inferences: Decision procedures Current and future work On Model-Based Reasoning 1 Recent Trends and Current Developments Maria

698 views • 43 slides
3 COMP 1 5 9 3 Algorithmic Verification Safety and Liveness, Fairness Dr. Liam OConnor

3 COMP 1 5 9 3 Algorithmic Verification Safety and Liveness, Fairness Dr. Liam OConnor

<latexit

851 views • 66 slides
3 COMP 1 5 9 3 Algorithmic Verification Safety and Liveness, Fairness Dr. Liam OConnor

3 COMP 1 5 9 3 Algorithmic Verification Safety and Liveness, Fairness Dr. Liam OConnor

<latexit

270 views • 16 slides
Leveraging GANs for fairness evaluations Emily Denton Research Scientist, Google Brain Emily

Leveraging GANs for fairness evaluations Emily Denton Research Scientist, Google Brain Emily

Leveraging GANs for fairness evaluations Emily Denton Research Scientist, Google Brain Emily Denton Margaret Mitchell Timnit Gebru Ben Hutchinson Background ML Fairness seeks to address algorithmic unfairness , with a focus on machine

446 views • 42 slides
Transparency and Fairness in Algorithms for Criminal Justice Cristopher Moore, Santa Fe Institute

Transparency and Fairness in Algorithms for Criminal Justice Cristopher Moore, Santa Fe Institute

Transparency and Fairness in Algorithms for Criminal Justice Cristopher Moore, Santa Fe Institute Kathy Powers, UNM Political Science Interdisciplinary Working Group on Algorithmic Justice Interdisciplinary Working Group on Algorithmic Justice

526 views • 33 slides
REForm: A Data Capture Framework for Large-scale Interventional Studies with Survey Workflow

REForm: A Data Capture Framework for Large-scale Interventional Studies with Survey Workflow

REForm: A Data Capture Framework for Large-scale Interventional Studies with Survey Workflow Management Shiqiang Tao, PhD N. Zeng, X. Wu, X. Li, W. Zhu, L. Cui, GQ Zhang Institute for Biomedical Informatics University of Kentucky

1.18k views • 21 slides
Non-parametric causal models Robin J. Evans Thomas S. Richardson Oxford and Univ. of Washington

Non-parametric causal models Robin J. Evans Thomas S. Richardson Oxford and Univ. of Washington

Non-parametric causal models Robin J. Evans Thomas S. Richardson Oxford and Univ. of Washington UAI Tutorial 12th July 2015 1 / 44 Structure Part One: Causal DAGs with latent variables Part Two: Statistical Models arising from DAGs with

1.2k views • 95 slides
Comparing covariate adjustment in interventional and observational studies Markus Kalisch,

Comparing covariate adjustment in interventional and observational studies Markus Kalisch,

Comparing covariate adjustment in interventional and observational studies Markus Kalisch, Seminar fr Statistik, ETH Zrich What is the total causal effect ? Treatment Outcome If we apply treatment , how will outcome

644 views • 39 slides
Requirements for Medical Physics Applications Joseph Perl Geant4 Collaboration Workshop

Requirements for Medical Physics Applications Joseph Perl Geant4 Collaboration Workshop

Requirements for Medical Physics Applications Joseph Perl Geant4 Collaboration Workshop Fermilab, 28 September 2015 Interventional External Brachy- Medical Nucl Medicine Beam Therapy therapy Imaging Interventional External Brachy-

764 views • 19 slides
High clinical utility the potential for fewer risky procedures and significant cost savings

High clinical utility the potential for fewer risky procedures and significant cost savings

High clinical utility the potential for fewer risky procedures and significant cost savings OncoCytes test could result in $ 2.2B to $4.7B in annual U.S. cost savings OncoCytes Test as part of Standard of Care Current Standard of Care

538 views • 6 slides
Appropriately select and tailor imaging to

Appropriately select and tailor imaging to

Appropriately select and tailor imaging to patient context and the clinical question(s). Provide timely, accurate and clinically useful reports on imaging studies. Appropriately manage imaging

607 views • 32 slides
GPUs for Medical Image Registration Raj Shekhar, PhD IGI Technologies (1 of 12 Emerging

GPUs for Medical Image Registration Raj Shekhar, PhD IGI Technologies (1 of 12 Emerging

GPUs for Medical Image Registration Raj Shekhar, PhD IGI Technologies (1 of 12 Emerging Companies at 2014 GPU Tech Conference) Misaligned images hinder precise, quantitative decision making Radiotherapy Interventional Radiology MR CT

198 views • 9 slides
Chapter 2 Process View of the Organization Process View of a Hospital Process Performance

Chapter 2 Process View of the Organization Process View of a Hospital Process Performance

Chapter 2 Process View of the Organization Process View of a Hospital Process Performance Process Flow Structures 2 1 Definition of a Business Process A process is a set of activities that

494 views • 13 slides

More recommend