Understanding how to manage the reputational risks of AI
The past few days have seen the Metropolitan Police in London, the FBI and US Immigration and Customs Enforcement (ICE) hauled over the coals for appearing to use inaccurate and non-consensual facial recognition technologies.
In the face of hostile media reports, public concerns about AI in general and complaints about their programmes specifically, as well as ongoing litigation, all three organisations have doubled down on the appropriateness and legality of their actions.
Their reaction is hardly surprising. The artificial intelligence (AI) that underpins these technologies is largely unregulated. And the general public is only starting to become aware of its benefits and risks, is largely skeptical of its promises, and is concerned about some of its potential impacts.
The looming tower of AI
The benefits of AI are many. It can help tackle climate change, strengthen cybersecurity, improve customer service and reduce the volume of abusive comments on Facebook, Instagram and other social media platforms, amongst all manner of other applications.
However, as Stanley Kubrick highlighted in his 1968 film 2001: A Space Odyssey in the form of HAL 9000, AI poses substantial risks.
These risks include:
- unfair or discriminatory algorithms
- unreliable or malfunctioning outcomes
- misuse of personal or confidential data
- greater exposure to cyberattacks
- loss of jobs
- legal risks and liabilities
- direct and indirect reputational risks, including malicious deepfakes.
It is likely that these risks will become greater and more reputational in nature as the adoption of AI technologies becomes more mainstream, awareness diversifies and grows, and public opinion consolidates.
In addition, the risk management industry is looking at AI from a risk perspective, and the PR/communications industry from a communications perspective.
AI reputation management research study
However, little exists on the reputational threats posed by AI, or how these should be managed should an incident or crisis occur – an important topic given the volume of AI controversies and the general focus on corporate behaviour and governance.
Accordingly, I am pulling together examples of AI controversies driven by or relating to artificial intelligence for an initial report, research study and white paper on the topic.
To kick-start the process, I am crowdsourcing information on the nature and impact of recent incidents through an AI and algorithimic incident and controversy repository.
The repository is open, and your contribution is welcome. Given the sensitivity of these types of events, please note all contributions should be fair, accurate and supportable.
Let me know if you have any questions.