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.
- The algorithm accepts input, and returns relevant output, but it is unknown how that output was produced.
- Lack of Transparency: Users cannot see how decisions are made, making it difficult to identify errors or biases.
- Accountability Issues: When an algorithm fails or causes harm, it's unclear who is responsible.
Facebook's News Feed Algorithm: The algorithm prioritizescontent based on userengagement, but the exactcriteriaare unknown, leading to concernsabout misinformationand echochambers.
Transparency in Algorithms
- Explainable: Making algorithms understandable to users and stakeholders.
- Open Source: Sharing the algorithm's code to allow for external review.
- Regulations: Laws like the GDPR in Europe require organizations to explain automated decisions to users.
Transparency is especially important in critical areas like healthcare, finance, and criminal justice, where algorithmic decisions can have significant impacts.
Erosion and/or Loss of Human Judgment
- Automation: Algorithms can replace human decision-making in areas like hiring, loan approval, and law enforcement.
- Overreliance: Trusting algorithms blindly can lead to errors and reduce human oversight.
- Loss of Skills: As algorithms take over tasks, humans may lose the ability to perform them manually.
Autonomous Vehicles: While self-driving cars can reduce accidents, overreliance on automation could diminish drivers' skills and judgment in emergency situations.
Theory of KnowledgeTo what extent should we trust algorithms to make decisions that affect people's lives?
Self review- Can you explain how algorithmic bias occurs and its impacts?
- What are the challenges of black box algorithms?
- How can transparency and accountability be ensured in algorithmic systems?
