Why Are Ethical AI, Data Privacy, and Trust Important Now?

The three robotics principles were developed by Isaac Asimov in his best-selling book I, Robot. A robot must not injure humans or enable them to be harmed by inaction under the first law. Human orders must be followed under the second law. Third law states that a self-harming robot must avoid self-harm unless required by the first or second laws. Asimov’s laws have permeated artificial intelligence, including business. Besides protecting AI workers, the legislation have spawned a debate about ethical AI.

Despite the debate over AI’s use in numerous businesses, ethical AI practices have been neglected. Why are AI ethics more important than ever? Women in AI and Emeritus just hosted a webinar on “Data Privacy, Trust, and Ethical AI.” Bhuva Subram, Women in AI’s North American regional head, was joined by One Creation Corporation co-founder and CPO Yang Cheung and ClearOPS Inc. co-founder and CEO Caroline McCaffery. The discussion focused on AI, machine learning, data science, and, most importantly, its integration with the UN Sustainable Development Goals (SDGs) to create global social change.

Describe ethical AI

Businesses globally use AI to improve services and gain a competitive edge. This is happening quickly. Harvard academics recommend monitoring, regulating, and humanising this expansion for long-term success. Responsible, accountable, and transparent AI implementation is necessary for ethical AI. This includes following laws, rules, standards, corporate principles, and consumer expectations.

Ethical AI now protects data and avoids biassed outcomes. Every data-supported decision must be justified and explained.

Define Data Privacy

The Subram: Digital transformation is accelerating in the post-pandemic new normal. How can we improve social participation data protection to emphasise human rights?

Cheung stated: Our lives are now all digital. Zoom meetings, WhatsApp conversations, Instagram clips, and Alexa culinary instructions are our current activities. Thus, much data is shared. This has caused many data breaches! Global legislation is tightening and emphasising self-governance.

Cisco found that 86% of respondents were concerned about privacy and data sharing. In contrast, 97% of respondents were uninformed of how firms use their data. Businesses should protect consumer data even while people have limited influence over it. Businesses must foster data privacy and protection.

Employees from entry-level to executive must follow this culture. Responsible data gathering should consider returning control to the data owner to sustain self-sovereignty.

What approaches eliminate data bias?

Subram: What artificial intelligence and machine learning methods help eliminate bias?

McCaffery has: Start by reviewing the model training data set to prioritise producing more benefit than harm. Companies must ensure bias-free information, which is easier said than done. AI engineers, deep learning researchers, and AI employees should examine this. The dataset’s size, visualisation potential, and the model’s personal influence on an individual are all important.

Eliminating bias is difficult but possible. Suppose a data set only includes 20- to 40-year-olds. That won’t work for an age model. We must include everyone from the oldest to the 120th. Maybe even more.

Can AI be humanised? How Could Ethical AI Be Built?

How can equity be integrated into machine learning and artificial intelligence and some algorithmic discrimination eliminated? Can ethics become the norm?

McCafferey says we need more data if the current ones aren’t diverse. That might ensure human interaction and eliminate discrimination. It will assure fairness and humanise the process. We may also need to build models that clean data sets from the start. Ethics should always be the first priority. Trust that regulators are considering it if firms are not. New York passed an AI bias statute that takes effect in January 2023. Since the hiring process involves more than an algorithm, AI cannot be used to evaluate job candidates and make recruiting decisions. Slowly spreading across Europe! This is commendable equity promotion in the fast-growing profession.

Online courses, free sources, and AI entry points abound. Yes, learning programming languages and doing AI projects are important. However, that is a very unique vista. You must think beyond the basics and understand your results. 

Where to start in AI?

The Subram: Beyond taking online classes, what advice would you give someone who wants to enter this profession or pursue this career path, academic or entrepreneurial?

In addition to technical skills, Cheung said interpersonal skills are crucial. That alone will enable ethical AI. Webinars and events help keep up with trends and improve interpersonal skills. Consider subscriptions to journals, podcasts, and blogs. Emerging technology, regulatory frameworks, and business trajectory must be understood.

Read More: “Exploring NoOps: The Evolution of DevOps in the Serverless Cloud”

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