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The truth of Algorithm auditing

Artificial intelligence systems (AI) with Algorithms in present world scenarios play an important role as AI outcomes as mathematical, programmatic, and perhaps, inherently better than emotion-laden human decisions however it often fails to differentiate between the loopholes that end us with non-beneficial decisions.

AI ethics draws an interdisciplinary group of intellectuals under whose influence Accenture Applied Intelligence has developed a fairness tool to understand and address bias in both the data and the algorithmic models that are at the core of AI systems. No matter how much we intend our technology to be objective-oriented, it is ultimately influenced by the people who build it and the data that feeds it.

Technologists do not define the objective functions behind AI independent of social context. Data is not objective, is it pensive of pre-existing prejudices. In custom, algorithms can be a method of immortalising bias, leading to unintended adverse consequences and unfair outcomes.

Algorithms have been continuously tampered with and one after another has codified and perpetuated it, as companies have simultaneously continued to more or less shield their algorithms from public scrutiny.

So now the question is HOW to regulate this issue and make it better?

The intellectuals have advocated and given inputs for algorithmic audits, which would dissect and stress-test algorithms to see how the algorithms are performing and to comply with their authenticity.

Moreover, there is a growing field of private auditing firms to do the same. Increasingly, companies are turning to these firms to review their algorithms, particularly when they’ve faced criticism for biased outcomes, but it’s not clear whether such audits are actually making algorithms less biased or mere PR stunt.

How does auditing work?

The system has 3 comprehensive steps which are as follows:

  • The first part studies the data for the user-defined sensitive variables on other variables. The tool identifies and quantifies what impact each predictor variable has on the model’s output.
  • The second part investigates the error in model classes and if there is a discernibly different pattern of the error terms for men and women, this is an indication that the outcomes may be driven by gender.
  • The third and final tool examines the false positive rate across different groups and enforces a user-determined equal rate of false positives across all groups. False positives are one particular form of model error: instances where the model outcome said “yes” when the answer should have been “no.”

Although it seems like a great solution, it merely depends on how the users use it.

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