The ethical risks of artificial intelligence are extensively documented, ranging from biased recruiting systems to racist chat bots and mistakes in auditing algorithms. Organizations are scrambling to grasp the ethical significance of programs as the threat of AI legislation looms. The embryonic AI auditing sector is emerging to accommodate this need. However, the absence of a common AI ethical system has resulted in uneven methods and concerns that AI auditing may not only fail to recognize unfairness but could even legitimize dangerous technology.

According to Emre Hagendorff, a UCL scholar and the founder of Integrative AI (an auditing and accounting startup), a divided methodology for AI audits has emerged. Around one end of the spectrum, there seems to be light-touch consulting, in which businesses want high-level advice, an ethical plan, or a brand enhancement. A forensic investigation, on the other hand, involves an audit investigating an industry’s sophisticated algorithms. Nevertheless, major consultants such as PwC and Deloitte are establishing themselves as prominent competitors.

According to McKee & Porter (2020), this is driving smaller competitors to distinguish themselves with specific specializations such as algorithm transparency and computational resilience. Some deliberately concentrate on ‘de-biasing’ since discrimination is a hot topic of focus. Many organizations also operate in the field of ‘interpretability,’ i.e., being able to transparently and accurately describe why an AI algorithm made the choice it did.