Part 2 - Risks of poor ML Governance

To connect the dots in case you have missed our other articles, please check out:

• Part 1 - Importance of ML Governance
• Part 2 - Risks of poor ML Governance (this article)
• Part 3 - ML Governance key principles and best practices
• Part 4 - A foray into Data Governance

Risks of poor ML Governance

Poor Machine Learning (ML) Governance can lead to a wide range of risks and negative outcomes, both for the organization deploying the ML models and for the individuals affected by their decisions. The most obvious risks of poor ML Governance include:

• Legal and compliance risks: Poor ML Governance can result in non-compliance with legal and regulatory frameworks such as data privacy laws, which can result in significant financial penalties and reputational damage. ML models that make decisions based on biased data or without appropriate transparency and explainability can also result in legal liability for the organization.
• Ethical and social risks: ML models that are not properly governed can have negative impacts on society, including perpetuating existing biases and discrimination, amplifying inequalities, and eroding trust in institutions. For example, in healthcare a ML model that is not properly trained on diverse and representative data may disproportionately misdiagnose certain groups, leading to unequal healthcare outcomes.
• Financial risks: Poorly governed ML models can result in financial losses for an organization. For example, an ML model deployed in a financial institution that is not properly validated may make erroneous predictions, leading to significant financial losses.
• Operational risks: An ML model that is not properly governed can also result in operational risks, such as failure to meet service level agreements or negative impact on the overall business processes.
• Reputational risks: An ML model that is not properly governed and produces incorrect or biased results can result in negative publicity, loss of customers, and damage to an organization's reputation.

What we see is that the risks of poor governance in ML are multifaceted and can have far-reaching consequences for organizations and society at large. Proper governance processes are essential to mitigate these risks and ensure the responsible and ethical use of ML.

Big question however is, whether and how can or shall this be ensured or enforced? Depending on where we look in this world, rules and regulations are put forward by governments. But to what degree these are enforced or adopted is a different discourse. Bottom line however is, organizations and their clients, whether in a Business to Business (B2B) or Business to Consumer (B2C) context, step into a relationship which is not only built on good services or products, but also by trust.

Trust as an opportunity

So while organizations may not sell trust as a product or service, trust is a crucial factor - an opportunity - that impacts consumer behavior and buying decisions. Organizations therefore influence client trust by building a trustworthy brand, providing reliable products and services, and establishing strong relationships with their clients. That creates a reputation.

And an organization's reputation for trustworthiness has a direct impact on its bottom line. Clients are more likely to purchase from organizations they trust, and they are willing to pay a premium for products and services from trustworthy companies. On the other hand, a lack of trust leads to lost sales, negative reviews, and damage to the organization's reputation. The same applies to the investor confidence in an organization. The more trustworthy an organization is perceived, the more it inspires investors to back an organization; hence build a stronger financial position.

Don't forget the data

So far so good, but there is also the client’s data an organization handles during their relationship. And this is often forgotten in the discourse. It is the trust a client puts into an organization to handle their data in a compliant, transparent and ethical manner the same way that organizations would apply insights from that data in order to improve their services and products.?

Therefore, organizations should have a vested interest in building and maintaining trust with their clients. Trust is the essential part of the relationship between them and their clients, and organizations that prioritize building trust are more likely to succeed in the long run. Good Data and ML Governance practices are key to embracing that trust. Regulations are there to guide organizations in a societal and legal context, but it is upon organizations to execute on these.

Follow us on OriginML for our next article on ML Governance key principles and best practices. Stay tuned.

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