AI governance is an overarching framework for administering its use and deployment by an organization using a broad set of processes, methodologies, and tools.
The goal of AI governance goes beyond ensuring the effective use of machine learning and deep learning. In fact, the scope is much broader and encompasses risk management, regulatory compliance and the ethical use of these technologies.
It is important to note the distinction between AI governance and AI regulation. This second concept refers to the laws and rules established for the use of artificial intelligence by a government or regulatory body and applying to all organizations under their jurisdiction. Instead, governance of AI refers to how it is administered in an organizational context.
Advantages and disadvantages of deep learning
Organizations already have mature IT governance practices. So why do they need AI governance? These two disciplines are close and share practices, but they should be differentiated because the level of maturity of companies is still too low.
In popular parlance, AI refers to deep learning; on the approaches to machine learning based on neural networks. The central idea of deep learning is that decision rules are derived from data and not hard-coded by humans, which is the norm in traditional computing systems. Dramatic improvements in accuracy and near-human performance are observed when these neural networks perform narrowly defined tasks in areas such as language processing, image recognition and speech recognition.
Automated decision-making systems that use these AI capabilities are becoming mainstream. Algorithms push shopping suggestions, news feeds, job applications, credit decisions, health recommendations, and more. The brought automation promises great benefits from a business perspective, but there are also downsides to consider. Unlike hard-coded rules, the “why” of a decision in a deep learning model is neither intuitive nor easy to understand. Hence the expression “AI is a black box”.
There are other limits than the lack of transparency:
- Things keep changing in the real world, and patterns or relationships that an AI system has learned may no longer be applicable.
- Real-world data is often different from that used to train AI models.
- AI models only work well for certain types of audiences, not all. This is called AI bias or algorithmic bias.
In all of these scenarios, the automated decisions are likely incorrect, but organizations continue to rely on them without correcting their algorithms.
The need for AI governance
With the growing adoption of AI comes increased recognition of its strengths and limitations. Governments are introducing new regulations and guidelines to prevent harm from intentional or unintentional misuse of artificial intelligence.
Improper use of this technology can expose an organization to operational, financial, regulatory and reputational risks. It’s also unlikely to align with your organization’s core values. The unique nature of AI requires safeguards to be put in place to ensure it works as intended. This is the key mandate of AI governance.
After a few years of experience implementing and scaling deep learning in the enterprise, AI governance playbooks and best practices are beginning to emerge. Here are some notable examples:
- Pharmaceutical company Novartis, which engaged a multidisciplinary team of experts to review their use of AI systems across the pharmaceutical value chain, and to draft their position on using AI responsibly and ethics, in a manner aligned with the company’s global code of ethics;
- The IEEE, the world’s largest technical professional organization for the advancement of technology, which created the Ethically Aligned Design business standards, covering the full gamut from the need for AI ethics in businesses with the skills and personnel required;
- The Montreal AI Ethics Institute, a non-profit organization, which regularly produces “State of AI Ethics Reports” and helps democratize access to knowledge about AI ethics. ethics of AI; and
- The Government of Singapore, which pioneered and released the Model AI Governance Framework, to provide practical guidance to the private sector on how to address ethics and governance issues in AI deployments. the AI.
AI governance is not the business of software engineers or machine learning experts alone. It is multidisciplinary and involves technical and non-technical stakeholders.
This discipline concerns end users in the public and private sectors, as well as AI software vendors. A few progressive organizations are even making AI governance an integral part of their corporate governance and CSR strategies, as it involves how an organization should implement AI ethics principles and ensure responsible use of AI.
About the Author :
Kashyap Kompella is an AI expert and founder of RPA2AI Research. RPA2AI is an analyst firm that advises companies, investment funds and government agencies on the adoption of AI.
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What is AI governance and why do you need it?
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