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06 Jul 2022

Ethical AI: The Key to a Secure Future

Omdia Analyst
Ethical AI: The Key to a Secure Future

Businesses and industry leaders alike are increasingly beginning to appreciate artificial intelligence (AI) ‘s benefits and recognise that adoption is essential to stay competitive.

Enterprise spending on robotic process automation (RPA) and intelligent automation will reach nearly $11.5 billion by 2026, according to Omdia IoT research. Omdia surveyed 5,000 enterprises and found that 70% of companies ranked AI and intelligent automation either "significantly more important" or "more important" to their operations after having encountered the COVID-19 pandemic. 

AI will undoubtedly transform the economy. The real debate is whether the change will be for better or worse.  As more organizations incorporate AI into their processes, leaders are taking a closer look at AI bias and ethical issues, looking for ways to use AI to solve business affairs while finding accountability for ensuring compliance.    

How do we define ethical AI? 

The ethics of artificial intelligence is a branch of the ethics of technology-specific to artificially intelligent systems, according to Vincent Müller, author of ‘Ethics of artificial intelligence and robotics.’ 

Ethics can sometimes be divided between concern for the moral behavior of humans as they design, make, use and treat artificially intelligent systems and a concern for the conduct of machines. Every stage of delivering your AI project should integrate considerations of the social and ethical implications of the design and use of AI systems.  

For AI to positively impact work, it must be respectful to its data and insights about. It must be transparent and understandable for everyone, which is when meaningful AI can be established. 

Meaningful AI  

This brave new, digitally interconnected world can deliver rapid gains in the power of AI to better society. Innovations in AI are already dramatically improving the provision of essential social goods and services from healthcare, education and transportation to the food supply, energy and environmental management.   

However, all AI projects need to be mindful of bias. Prejudiced datasets can cause problems, from criminal justice algorithms disliking offenders who are ethnic minorities to biased recruitment tools against hiring women. 

Elenn Stirbu, director of Global Community Sourcing at Lionbridge AI, claims in the ‘AI starts with data’ eBook that everyone should work around biases rather than eliminating them. Stirbu notes that “all data is biased” and suggests that the real challenge is to have enough data to represent all the biases that a project can encounter so that all demographics are represented, such as age ranges, genders, ethnicities and skin tone.  

Only then will a project produce enough training output data that is fair to everyone, provide a quality customer experience and resolve identity crises such as facial recognition technology. Government affairs are also crucial in ethical AI as they can shape the legal frameworks that businesses operate in for the future and currently.  

Governance of AI and regulatory standards  

According to Omdia's AI Processors for Cloud and Data Center Forecast Report, the AI processing and associated AI hardware market in the cloud and data centers continues to expand at a blistering pace, and the applications of AI are constantly growing. This puts pressure on governments worldwide to create frameworks that would contain AI and ensure correct practices.  

September 2021 saw the U.K. government publish its 10-year strategy on artificial intelligence. The AI Strategy recognizes that building a trusted and pro-innovation system necessitates addressing AI’s potential risks and harms. These include concerns around fairness, bias, accountability, safety, liability, and transparency of AI systems. 

However, in this industry, the EU and the U.S are arguably two of the most influential players. Both are world leaders in designing and using AI technologies, so their efforts to govern them will have a global impact. Recently, the EU Commission published its prospective ‘AI Act’. Among the elements of the act is a risk-based approach to governing AI. Through this risk-based framework and previous governance initiatives, European values can be promoted, with fundamental rights protected, economic outcomes improved and social disruptions minimized. The U.S. has tended to hesitate in introducing legal restrictions similar to those of the EU over fears such regulatory measures could potentially hinder innovation.   

An effective AI governance system is crucial for securing AI's future since it can establish the standards that will unify accountability and ethics as the technology develops. 

Not a one size fits all solution – further insight required  

AI is here to stay, and ethics around it are vital. AI will contribute significantly to modern society, whether in healthcare, cybersecurity, sustainability, increased food production or elsewhere. However, the core principles of Ethical AI and adhering to government frameworks might not be enough for businesses.  

Recent Omdia data shows that larger companies ($1 billion or more in revenues) are currently significantly more AI Ready than companies with less than $1 billion revenues. To be fully AI-ready, businesses and governments must invest heavily in future-oriented work strategies, including education, re-training and skill development.  

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