Ethical AI Lapses Happen When No One Is Watching

Transparency often plays a key role in ethical business dilemmas — the more information we have, the easier it is to determine what are acceptable and unacceptable outcomes. If financials are misaligned, who made an accounting error? If data is breached, who was responsible for securing it and were they acting properly?

But what happens when we look for a clear source of an error or problem and there’s no human to be found? That’s where artificial intelligence presents unique ethical considerations.

AI shows enormous potential within organizations, but it’s still largely a solution that is looking for a problem. It’s a misunderstood concept with practical applications that have yet to be fully realized within the enterprise. Coupled with the fact that many companies lack the budget, talent, and vision to apply AI in a truly transformational way, AI is still far from critical mass and prone to misuse.

But just because AI may not be ultra-visible within day-to-day business doesn’t mean it isn’t at work somewhere within your organization. Just like many other ethical dilemmas in business, ethical lapses in AI often happen in the shadows. Intentional or not, the consequences of an AI project or application breaking ethical boundaries can be a logistical and optical nightmare. The key to avoiding ethical missteps in AI is to have corporate governance of the projects from the get-go.

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Mots-clés : cybersécurité, sécurité informatique, protection des données, menaces cybernétiques, veille cyber, analyse de vulnérabilités, sécurité des réseaux, cyberattaques, conformité RGPD, NIS2, DORA, PCIDSS, DEVSECOPS, eSANTE, intelligence artificielle, IA en cybersécurité, apprentissage automatique, deep learning, algorithmes de sécurité, détection des anomalies, systèmes intelligents, automatisation de la sécurité, IA pour la prévention des cyberattaques.