Algorithmic Dilemmas
- Algorithmic dilemmas are challenges and ethical issues that arise from the use of algorithms.
- While algorithms offer efficiency and consistency, they can also lead to unintended consequences, such as bias, lack of transparency, and the erosion of human judgment.
Algorithmic Bias and Fairness
A systematic error in a computer system and/or algorithm that creates unfair outcomes, such as, but not limited to, privileging one group over others.
Causes of Algorithmic Bias
- Biased Data: Algorithms trained on historical data that reflect societal biases will perpetuate those biases.
- Design Choices: Developers may unintentionally embed their own biases into the algorithm.
- Lack of Diversity: Homogeneous teams may overlook biases that affect underrepresented groups.
Amazon's Recruitment Tool: In 2018, Amazon had to scrap a recruitment algorithm that favouredmale candidates because it was trained on historicaldata from a male-dominatedworkforce.
Fairness in Algorithms
- Equal Treatment: Ensuring that the algorithm performs consistently across all groups.
- Outcome Fairness: Ensuring that the results of the algorithm are equitable for all groups.
- Bias Audits: Regularly testing algorithms for bias and making adjustments as needed.
COMPAS Algorithm: Used in the U.S. for criminal risk assessment, it was found to disproportionatelyassignhigher risk scores to Blackdefendants, leading to harshersentences.
Algorithmic Accountability and Transparency
The responsibility of developers and organizations to ensure that algorithms are fair, transparent, and explainable.
Black Box Algorithms
- An algorithm whose internal workings are not visible or understandable to users.
