Why Business Leaders Should Care About AI Transparency
Let’s start with the important question:
Do you understand and trust your AI?
As Evert Haasdijk, Senior Manager and member of Deloitte’s AI Centre of Excellence bluntly puts it:
“The board room and higher management of a company are often not really aware what developers in the technical and the data analytics departments are working on. They have an idea, but they don’t know exactly. This causes risks for the company.”
To make progress, business leaders across all industries must take the necessary steps to understand the inner workings of their AI model. There are several ways in which AI transparency can propel a business forward.
With many public examples of AI decision-making going awry, public anxiety regarding the use of machine learning is on the rise.
From legal concerns regarding the use of personal data, to ethical concerns on the matter of fairness, the influence of AI models on high-impact decisions is under continuous scrutiny.
After all, giants like Apple and Amazon have unknowingly deployed biased AI models to make processes such as credit application and recruitment more efficient. The case of COMPAS, a tool used by US courts to predict the potential risk of recidivism in defendants, is notorious.
Machine Learning bias is a complex problem that stands at the crossroads between mathematical analysis and the social implications of determining what is ‘fair’ in decision-making. In these circumstances, the expectation is for AI to produce transparent and understandable results that reflect the ethical standards of society.
Transparent AI allows businesses and organizations to offer meaningful and understandable explanations that humans can trust.
On top of building trust, insights gained by means of AI transparency can be used to identify weak points and failure cases that can be subsequently used to improve the accuracy and robustness of machine learning models.
Machine learning models can pick up spurious correlations and appear to be making the correct decisions, but for the wrong reason. A famous example is a model trained to distinguish between photos of wolves and huskies, which learned to predict “wolf” whenever it saw snow in the background, just because in the training dataset most pictures of wolves had snow, while most pictures of huskies did not.
As such, AI transparency promotes continuous optimization and enables a symbiotic relationship to be formed between the human and the machine.
As machine learning is becoming a core element in the value proposition of organizations worldwide, the number of threats is also increasing. Malicious attackers often target AI models by means of adversarial attacks.
Be it in the form of poisoning, evasion, or model extraction, an adversarial attack can lead to partial or irrevocable damage, from data theft up to complete model decay.
By means of AI transparency, business leaders, and data scientists could combat the potential threat of malicious attacks.
Big industry names such as Google, Microsoft, and IBM have already started to invest both in developing AI models, but also in securing them against adversarial attacks.
Implementing the necessary security measures for AI models should be a crucial step in every business’s defense strategy.
Due to the potential biases that AI models may include in their decision-making processes, many countries around the globe have started implementing AI strategies and policies meant to ensure the ethical use of algorithms.
The OECD AI Policy Observatory provides a repository of over 700 policies initiated by 60 countries.
Perhaps some of the best-known legal frameworks that businesses and organizations employing AI models need to take into consideration are:
As Machine Learning technologies are becoming essential to advancing in the digital landscape, AI regulations are here to stay. The question is: Is your business prepared for them?
Embracing AI has gained a new nuance of urgency, especially in the context of a post-pandemic economy. A major study of 900 senior executives conducted by HFS Research in conjunction with KPMG shows how AI has become crucial to the future survival of businesses.
Fulfilling the positive potential of AI is ultimately only possible by opening the black box and experimenting toward success. It is only through AI transparency and explainability that we can give Machine Learning strategies purpose across all the important dimensions, those of trust, performance, security, and compliance.
Lumenova AI can help companies understand the inner workings of their AI models, so they can efficiently mitigate bias and ensure compliance with new and emerging algorithmic regulations.
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