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Put simply, AI bias refers to discrimination in the output churned out byArtificial Intelligence (AI)systems.
These biases often reinforce existing social inequalities.
These, he says, lead to disparate performance across racial and gender groups.
Owing to this bias, AI models may generate text or images that reinforce stereotypes about gender roles.
AI-generated outputs also may reflect cultural stereotypes, says Sergiienko.
How do biases creep into LLMs?
Sergiienko says there are several avenues for biases to make their way into LLMs.
Algorithmic bias:The methods used in AI model training may amplify pre-existing biases in the data.
Implicit associations:Unintended biases in the language or context within the training data can lead to flawed outputs.
Can AI ever be free of bias?
Our experts believe the complete transcendence of human biases may be an elusive goal for AI.
He says the key to reducing bias lies in striving for AI that complements human decision-making.
This will help leverage the strengths of both while implementing robust safeguards against the amplification of harmful biases.
However, before bias can be removed from LLMs, it is important to first identify it.
Masood says this calls for a varied approach that uses numerical data, expert analysis, and real-world testing.
However, unlike a one-time task, identifying bias is an ongoing process.
Masood points to various research efforts and benchmarks that address different aspects of bias, toxicity, and harm.
Furthermore, businesses must create ethical review boards to scrutinize training data and model outputs.
Finally, they should also invest in conducting third-party audits to independently verify fairness claims.
He also suggests businesses collaborate with AI researchers, ethicists, and domain experts.
This, he believes, can help surface potential biases that may not be immediately apparent to technologists alone.
Implementretrieval-augmented generation (RAG):This model architecture combines retrieval-based techniques with generation-based techniques.
It pulls relevant data from external sources before generating a response, providing more accurate and contextually grounded answers.
System prompt review and refinement:This can help prevent models from unintentionally generating biased or inaccurate outputs.
Regular evaluation and testing:Businesses must continuously monitor AI outputs and run test cases to identify biases.
But mitigating bias is like walking a tightrope.
Saiz says this poses a question: should LLMs be trained for truth-seeking?
Or is there potential in building an intelligence that doesnt know of, or learn from past mistakes?
There are pros and cons to both approaches, says Saiz.
Ideally the answer is not one or the other, but a combination of the two.
