Fairness Evaluation
Fairness Evaluation - Related Videos
14:32
Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
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Cited by 26 •
2011
This paper illustrates an innovative visual approach (photovoice or photo-elicitation) to achieve fair process in clinical care for patients living with chronic health conditions, illuminate gaps in clinical knowledge, forge better therapeutic relationships, and identify patient-centered goals and possibilities for healing.
01:22
Fair Value Principle
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2025
The fair value principle in accounting mandates measuring and reporting assets and liabilities at their current market value rather than their historical purchase cost. This principle aims to present a more accurate and timely reflection of an entity’s financial position, particularly in environments characterized by fluctuating market conditions. Fair value accounting is especially relevant for financial instruments and other assets whose values are sensitive to market dynamics.Under fair...
01:29
Cost Basis vs. Fair Value Accounting
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2025
Cost basis and fair value accounting represent two contrasting methods for valuing assets, each with distinct implications for financial reporting and decision-making. These methods not only influence how companies report their financial position but also affect investor perceptions and regulatory compliance.Cost basis accounting relies on the historical cost principle, meaning assets are recorded at their acquisition price and remain unchanged over time unless impaired. This approach is valued...
08:04
Evaluating the Accuracy of Snap Judgments
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2023
Source: Diego Reinero & Jay Van Bavel—New York University Social psychologists have long been interested in the way people form impressions of others. Much of this work has focused on the errors people make in judging others, such as the exaggerated influence of central traits (such as "warm" and "cold"), the insufficient weight given to the context in which others' behavior takes place, and the tendency for people to make judgments that conform to their initial expectations about another.
03:00
Self-Evaluation: Self-Enhancement and Self-Verification
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2020
Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
FAQs
Different criteria emphasize different aspects of equity, including selection rates, error rates, calibration, or treatment across groups. A system may appear acceptable under one measure while showing disparities under another, so engineers need to define the relevant criterion before interpreting results. Comparing fairness measures alongside accuracy, safety, and robustness helps expose trade-offs rather than treating performance as a single outcome.
Bias may be introduced at several stages, including data collection, feature design, model training, and deployment. Evaluating these stages separately helps engineers connect an observed disparity with a possible source instead of treating the final model as the only cause. This staged view supports targeted responses, such as improving the dataset, adjusting the model, or changing the decision policy.
Group comparisons reveal whether a system distributes decisions, errors, calibration, or treatment differently among the people it affects. Looking only at aggregate performance can conceal disparities that become visible when results are separated by relevant demographic or other protected groups. This comparison gives engineers evidence for examining potential inequities before the system influences real-world decisions.
A practical evaluation begins by identifying the relevant groups and defining the fairness criteria to examine. Engineers then inspect the available data, model decisions, and selected performance measures, comparing quantities such as selection rates, error rates, calibration, or treatment. They interpret those findings with accuracy, safety, and robustness results, then consider appropriate mitigation before deployment or continued use.
Fairness evaluation is useful before deployment and whenever engineers assess how a system may affect real-world decisions. Applying it across development can help identify problems associated with data collection, feature design, training, or deployment rather than discovering them only after use. The results can guide dataset improvements, model adjustments, or changes to decision policies.
In software engineering and machine learning, the evaluation provides structured evidence about how decisions and performance measures differ across relevant groups. Engineers can use that evidence to identify bias, examine trade-offs with accuracy, safety, and robustness, and select mitigation strategies. Its broader value is supporting more accountable technologies by making potential disparities visible before they affect people.