How to make ethical AI a reality?

Artificial intelligence is democratizing at breakneck speed and slowly integrating into the fabric of our existence. Thanks to multiple advancements in AI, improved deep learning and the availability of high-performance computing, it is now possible to launch use cases that were previously unimaginable. For companies, the stakes are immense. It is therefore crucial to establish a vision, instill appropriate frameworks and policies, and develop adequate checks and balances, to protect individuals.

Principles and tools of ethical AI

Data governance frameworks and tools
A data governance structure, which revolves around data quality checks, historical and traceability analyses, and access control has become imperative. Investments in accelerators and in-house solutions for privacy management, such as data management, data security management (and in transit) and versioning for tracking, could also be a essential guarantee to avoid any violation.

Bias detection
An unbiased dataset is a necessary prerequisite for an AI model to make reliable and non-discriminatory predictions. For example, a car insurance company defaulted to classifying men under 25 as reckless drivers – partly due to an inherent historical bias in datasets regarding color, age and gender.

Appropriate tools for data quality control and assessment of model weaknesses, as well as definition of metric to measure the humility of the models, could contribute to highlighting the potential failures. Champion/Challenger methods, certain tests and corrective measures should be directly integrated into the model development process; in the same way as the training of data scientists in order to improve the interpretability of the models in order, ultimately, to improve their auditability.

Explainable and reproducible AI
Engineers are greatly interested in Explainable AI (XAI) to better understand the thought path of an AI model. It is essentially about understanding the decisions or predictions made by an AI, in order to alleviate concerns about unfairness. XAI algorithms allow better transparency, interpretability and provide clear and defined explanations. Its other objective is reproducibility, so that the predictions of ML models are consistent every time, even with new data sets. Tools and processes for performing in-depth audits and what-if analyzes can explain why and how they reached certain conclusions. This not only helps alleviate potential employee concerns, but also leads organizations to think carefully about how their assumptions are made. Along the same lines, ethical AI should also clearly articulate the benefits and management of data collection.

People at the heart of critical situations
Although AI models are built to operate autonomously without human intervention, human involvement is imperative in some cases. Especially in law enforcement. In several forensic applications, jail sentences handed down by an AI system have been reported to have twice the false positive rate for some ethical groups. Therefore, AI models must be risk-proofed, involving humans for all key decisions and having an effective fallback mechanism should the AI ​​system need to be circumvented.

The Building Blocks of a Solid Foundation of Ethical AI
A strong ethical framework for AI that complies with local privacy laws could allay fears of breaches and increase trust in current systems. There are several tech players today who have established AI codes of ethics to ensure that their AI efforts are fair, socially beneficial, responsible, and privacy-friendly. To build trust and accountability across the entire stakeholder ecosystem, a framework should evaluate AI systems against the criteria of fairness, explainability, reproducibility, safety, and usefulness.

The Ethical AI Framework should be created based on 6 broad core principles to mitigate the potentially negative effects of AI on society while maximizing long-term value creation. These include inclusion (both in empowerment to leverage AI technology and representation in design), socially beneficial and sustainable, employment dynamics while promoting requalification, equity by rejecting social biases and cultural denigration, data protection and privacy, and accountability and explainability. A company should actively promote inclusive discussions and deliberations among industries, communities, regulators, and academia on the challenges, benefits, costs, and consequences of AI, as well as influencing governance and regulations strong around the responsible and sustainable use of AI.

Self-regulation is also essential. An organization’s internal teams must be sufficiently empowered to ensure the protection of privacy and prevent certain biases in algorithmic decisions.

Today, AI is having a huge impact on redefining our economies, societies and political systems. Therefore, AI-related concerns are legitimate. They must now be addressed by all stakeholders in order to develop an ethical, responsible and sustainable AI ecosystem.

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