What is Algorithmic Accountability?
The concept of 'Algorithmic Accountability' holds companies accountable for the outcomes of the algorithms they create. It holds the company that created the algorithm morally and ethically liable for any problems caused by the algorithm.
Simply put, algorithmic accountability, also known as algorithmic transparency, is a policy measure that holds the developers and operators of automated decision-making systems accountable for the outcomes of their pre-programmed decision-making systems. Creating an algorithm entails a human inputting a specific set of instructions to be executed in a specific order to produce a specific result.
What is the process of Algorithmic Accountability?
Algorithmic accountability is accomplished in multiple steps:
- Decision Scrutiny: Examining algorithmic decisions on a regular basis, similar to evaluating a robot's choices.
Making sure decisions are reasonable, fair, and unbiased. - Learning Analysis: The study of how algorithms learn from previous data and experiences.
Preventing algorithms from producing errors or displaying prejudice. - Rule Implementation: Providing unambiguous rules for algorithms to follow.
Making certain that algorithms are ethical and respect privacy.
What is the significance of Algorithmic Accountability?
Algorithmic Accountability is critical for the following reasons:
- Equality and fairness: Keeping algorithms from treating different people unfairly.
Similar to a teacher fairly rating all students.
Example: Social media platforms use algorithms to monitor content. Accountability ensures that these algorithms do not unfairly censor or promote content based on biased criteria.
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- Error Prevention: Preventing algorithms from making incorrect decisions as a result of defects in their learning.
Preventing unforeseen and undesirable consequences.
Example: Self-driving car algorithms are continually tested and updated to prevent accidents caused by software errors, ensuring safety and reliability.
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