Artificial intelligence (AI) is already impacting our daily lives in ways we never imagined just a few years ago – and in ways we don’t know today. From self-driving cars to voice-assisted devices to predictive text messaging, AI has become a necessary and unavoidable part of our society, including in the workplace.
Data shows that the use of AI in business is increasing. In 2019, a Gartner report indicated that 37% of organizations had implemented AI to some extent. More recently, Gartner predicted that the global AI software market would reach $62.5 billion by the end of this year, a 21% jump from the previous year.
While the impact of AI is undeniable, consumer concerns about the ethics and safety of AI technology persist. For this reason, companies should strive to mitigate these concerns by always protecting customer data when using AI-based technology.
The need for responsible AI
Any consumer-facing organization that uses AI technology must act responsibly, especially when customer data is involved. Technology leaders using AI must at all times focus on two responsibilities: reducing model bias and maintaining data confidentiality and confidentiality.
[ Also read Artificial intelligence: 3 ways to prioritize responsible practices. ]
In addition to ensuring data security, responsible AI practices should eliminate biases embedded in the models that feed them. Companies should regularly assess any biases that may be present in their vendors’ models, then advise customers on the most appropriate technology for them. This monitoring should also correct biases with pre- and post-processing rules.
While companies cannot remove the biases inherent in AI systems trained on large amounts of data, they can work to minimize adverse effects. Here are some best practices:
1. People first
AI can be beneficial in reducing the amount of repetitive work done by humans, but humans should always come first. Create a culture that doesn’t involve a storyline between AI and humans. Harness the creativity, empathy and dexterity of human teams and let AI create more efficiency.
Harness the creativity, empathy and dexterity of human teams and let AI create more efficiency.
2. Consider data and privacy goals
Once the goals, long-term vision and mission are in place, ask yourself: what does the company own? There are many basic models or solutions that can be used without any training data, but in some cases the degree of accuracy can be much higher.
Tailoring AI systems to business goals and data will yield the best results. Done correctly, data preparation and cleaning can eliminate bias during this step. Eliminating data bias is essential to developing responsible AI solutions. You can remove features that impact the overall result and further perpetuate existing biases.
In terms of confidentiality, commit to protecting all the data you collect, regardless of its quantity. One way to achieve this is to only work with third-party vendors who strictly adhere to the stipulations of crucial pieces of legislation, such as GDPR, and maintain critical security certifications, such as ISO 27001. Compliance with these regulations and obtaining these certifications takes a long time. effort, but they demonstrate that the organization is qualified to protect customer data.
3. Implement active learning
Once a system is in production, provide human feedback on technology performance and biases. If users detect that the output differs depending on the scenario, create guidelines to report and resolve these issues. This can be done at the core of the AI system as a correction to the output.
In recent years, some of the world’s largest organizations, including Google, Microsoft, and the European Commission, have developed frameworks and shared knowledge on their Responsible AI guidelines. As more organizations embrace responsible AI-related business language, it will become the expectation of partners and customers.
When a mistake can cost your brand millions of dollars or ruin its reputation and relationship with employees and customers, extra support helps. No one wants to work with an organization that neglects customer data or uses biased AI solutions. The sooner your organization solves these problems, the more consumers will trust you and the benefits of using AI will begin to be felt.
[ Check out our primer on 10 key artificial intelligence terms for IT and business leaders: Cheat sheet: AI glossary. ]
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