The fallout from a case of ethical blindness can be just as damaging as a high-profile lawsuit, in terms of brand identity and reputational damage.
Facing tough questions
In the world of AI, data can assume a life of its own. In today’s world, the origin of data, the assumptions underlying it and the scrutiny which it has traditionally undergone in a company during decision-making are all now concealed within the algorithm.
Constant, high-profile media coverage about improper use or misuse of data collected by companies is now the norm. Personal or proprietary data is passed to third parties, either deliberately or by accident. There are hacks, leaks and security breaches, and decisions come to light that display racial or gender bias. In short, data security appears to be broken.
Regulators and consumer supervisors are now asking some tough questions to businesses: How do companies manage data for their own benefit and growth while respecting individuals’ rights to privacy and other companies’ intellectual and data property? Will they self-regulate? And if they will not, how can they be constrained? And with such questions rising, governments everywhere are gearing up for a new wave of regulation, the likes of which we have not seen since the last financial crisis.
What to do
For companies, the complexity of this shifting landscape means that a sharp-eyed focus on legal compliance is the bare minimum investment that a company should make. But the bigger issues are that the law is often not clear and the public is pushing for companies to have greater accountability toward the society. The fallout from a case of ethical blindness can be just as damaging as a high-profile lawsuit, in terms of brand and reputational damage.
How can companies protect themselves from AI risk?
- Firstly, they need to make sure that they are legally protected. The legal framework in which companies operate may be in a flux, but companies can still structure their contractual relationships so that employees, clients and third-party business partners have “signed up” to safeguard the information that is proprietary or subject to privacy rules.
- Secondly, data management systems need to be highly sophisticated. Algorithm monitoring and auditing systems, and advanced data analytics are required to understand where a unit of data is coming from and where it is going. Today, AI needs to be devised and developed in order to audit and monitor the AI that is already in use.
- Thirdly, companies need to invest in human resources. All employees, and especially company leadership, need to be aware of how AI affects both the business and stakeholders. The ambition to succeed in managing AI will be achieved only by the companies’ ambition to recruit a team of the right calibre to make this happen.
- Fourthly, companies are not islands. There are a number of interesting platforms in which these issues are being actively discussed from both the business’ and society’s point of view. By pooling resources and brainpower, companies are likely to find commercial applications for AI faster and more efficiently, without compromising their corporate values and integrity.
The law is often not clear and the public is now pushing for companies to have greater accountability and transparency in the society. In response, companies are now focusing on legal protections, highly sophisticated data management systems, human resources and partnerships to combat risks.