RAWLSNET: Altering Bayesian Networks to Encode Rawlsian Fair Equality of Opportunity

Liu, David; Shafi, Zohair; Fleisher, Will; Eliassi-Rad, Tina; Alfeld, Scott

doi:10.1145/3461702.3462618

Cited by 5 publications

(2 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, strategic classification (e.g, (Miller et al, 2020;Hardt et al, 2016a;Dong et al, 2018;Hu et al, 2019;Ahmadi et al, 2021;Milli et al, 2019;Kleinberg and Raghavan, 2019;Frankel and Kartik, 2021)) shows how people's reactions to the decision-making model highlights the differences in access through costs people incur. Although our framing of obstacles is quite analogous to budget framing in strategic classification, we formulate obstacles more generally, and the alleviation of obstacles strictly makes things better, that is to say, the obstacle-free feature values, z dominate the obstacle-refrained feature values x and the obstacle-free label y is not necessarily equal to the obstacle-refrained label, y. Relatedly, another body of work that highlights obstacles individuals face is causal and Bayesian inference (e.g., (Kilbertus et al, 2020;Madras et al, 2019;Makhlouf et al, 2020;Liu et al, 2021)). However, instead of ensuring equal access, the focus is mainly on redefining decision-making models, by, for example, changing accuracy metrics, weights of different features, or features used, among other interventions to qualify obstacle-refrained individuals.…”

Section: Related Workmentioning

confidence: 99%

“…Although several ML fairness research, for example, causal and Bayesian inference (e.g., Kilbertus et al (2020); Madras et al (2019); Makhlouf et al (2020); Liu et al (2021)), fair decision-making (e.g., Celis et al (2020); Kleinberg and Raghavan (2018); Emelianov et al (2020)) acknowledge obstacles individuals face, the focus is mainly on changing decision-making models, for example, to qualify obstacle-refrained individuals. We, however, argue that this only helps in the short and not the long term because unalleviated obstacles individuals face in accessing the model resurface in the model utilization, in which decision-makers evaluate individuals on how well they utilize the model as a form of feedback to the decision-makers.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Equity Framework: Fairness Beyond Equalized Predictive Outcomes

Naggita¹,

Aguma²

2022

Preprint

View full text Add to dashboard Cite

Machine Learning (ML) decision-making algorithms are now widely used in predictive decisionmaking, for example, to determine who to admit and give a loan. Their wide usage and consequential effects on individuals led the ML community to question and raise concerns on how the algorithms differently affect different people and communities. In this paper, we study fairness issues that arise when decision-makers use models (proxy models) that deviate from the models that depict the physical and social environment in which the decisions are situated (intended models). We also highlight the effect of obstacles on individual access and utilization of the models. To this end, we formulate an Equity Framework that considers equal access to the model, equal outcomes from the model, and equal utilization of the model, and consequentially achieves equity and higher social welfare than current fairness notions that aim for equality. We show how the three main aspects of the framework are connected and provide an equity scoring algorithm and questions to guide decisionmakers towards equitable decision-making. We show how failure to consider access, outcome, and utilization would exacerbate proxy gaps leading to an infinite inequity loop that reinforces structural inequities through inaccurate and incomplete ground truth curation. We, therefore, recommend a more critical look at the model design and its effect on equity and a shift towards equity achieving predictive decision-making models.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Equity Framework: Fairness Beyond Equalized Predictive Outcomes

Naggita¹,

Aguma²

2022

Preprint

View full text Add to dashboard Cite

show abstract

Algorithmic fairness datasets: the story so far

Fabris

Messina

Silvello

et al. 2022

Data Min Knowl Disc

View full text Add to dashboard Cite

Data-driven algorithms are studied and deployed in diverse domains to support critical decisions, directly impacting people’s well-being. As a result, a growing community of researchers has been investigating the equity of existing algorithms and proposing novel ones, advancing the understanding of risks and opportunities of automated decision-making for historically disadvantaged populations. Progress in fair machine learning and equitable algorithm design hinges on data, which can be appropriately used only if adequately documented. Unfortunately, the algorithmic fairness community, as a whole, suffers from a collective data documentation debt caused by a lack of information on specific resources (opacity) and scatteredness of available information (sparsity). In this work, we target this data documentation debt by surveying over two hundred datasets employed in algorithmic fairness research, and producing standardized and searchable documentation for each of them. Moreover we rigorously identify the three most popular fairness datasets, namely Adult, COMPAS, and German Credit, for which we compile in-depth documentation. This unifying documentation effort supports multiple contributions. Firstly, we summarize the merits and limitations of Adult, COMPAS, and German Credit, adding to and unifying recent scholarship, calling into question their suitability as general-purpose fairness benchmarks. Secondly, we document hundreds of available alternatives, annotating their domain and supported fairness tasks, along with additional properties of interest for fairness practitioners and researchers, including their format, cardinality, and the sensitive attributes they encode. We summarize this information, zooming in on the tasks, domains, and roles of these resources. Finally, we analyze these datasets from the perspective of five important data curation topics: anonymization, consent, inclusivity, labeling of sensitive attributes, and transparency. We discuss different approaches and levels of attention to these topics, making them tangible, and distill them into a set of best practices for the curation of novel resources.

show abstract

A systematic review of fairness in machine learning

Rabonato,

Berton

2024

AI Ethics

View full text Add to dashboard Cite

RAWLSNET: Altering Bayesian Networks to Encode Rawlsian Fair Equality of Opportunity

Cited by 5 publications

References 18 publications

The Equity Framework: Fairness Beyond Equalized Predictive Outcomes

The Equity Framework: Fairness Beyond Equalized Predictive Outcomes

Algorithmic fairness datasets: the story so far

A systematic review of fairness in machine learning

Contact Info

Product

Resources

About