Algorithmic decision-making in hiring, lending, and criminal justice raises fairness concerns. Advocates argue algorithms can reduce human biases—consistent rules rather than intuitive judgments. Critics counter that algorithms can encode and amplify existing biases from training data, potentially automating historical discrimination at scale. Some researchers now advocate for 'algorithmic auditing' to identify and correct these encoded biases.
Based on the passage, it can be inferred that
algorithms are always more biased than human decision-makers
algorithms cannot encode any human biases
whether technology reduces or amplifies bias may depend on how it is designed and monitored
algorithmic auditing has already solved all fairness problems
Correct Answer: C
Choice C is the best answer. Algorithms can reduce or amplify bias depending on implementation.
- Context clues: Advocates say algorithms reduce bias; critics say they can amplify it; auditing can correct.
- Meaning: The outcome depends on design and oversight.
- Verify: Auditing to "identify and correct" implies bias isn't inevitable but requires attention.
đŸ’¡ Strategy: When an outcome goes either way depending on implementation, infer design dependence.
Choice A is incorrect because advocates claim algorithms can reduce bias. Choice B is incorrect because critics show algorithms can "encode and amplify" biases. Choice D is incorrect because auditing is advocated as a solution, not claimed as solved.