Engineers, Strategists, Users, Ethicists

AI requires everyone's involvement

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Leading a team or organization in the age of artificial intelligence requires more than just technical talent; it also requires business acumen, empathy, foresight, and ethics.

The key is to understand what AI means. This doesn't mean that AI-powered solutions are introduced into organizations expecting miracles to happen overnight. According to his MIT Senior Lecturer Paul McDonagh-Smith, quoted in the MIT Sloan Management Review, AI is not a combination of humans and machines; strengthen our capabilities and support us across existing and new tasks.

People outside the AI ​​technology bubble need a solid understanding of how AI works and what it takes to make it responsible and productive. It requires training and a positive culture. People need to be informed about emerging technologies that are redefining their jobs and organizations in some way. AI skills aren't as plentiful, so organizations need programs to upskill employees and train them in technical skills and technical decision-making, McDonough-Smith says. Culture is a big part of the equation. Organizations need to create cross-functional teams that break down silos, tolerate failure to foster creativity, and encourage innovative ways to combine human and machine capabilities in complementary systems.

Industry leaders from a variety of fields echo the views highlighted in Management Review articles. In addition to business knowledge and a culture of innovation, ethics must also be a top consideration. This will require more diverse leadership of AI initiatives from business strategists, users, technologists, and people from the humanities.

Fundamentally, we are experiencing a once-in-a-decade moment, and how we lead people to embrace it will shape the AI ​​revolution for years to come, said Mozilla Foundation President and CEO said Executive Director Mark Surman.

The people running AI today are project managers and product managers running delivery, data engineers and scientists building data pipelines and models, and DevOps and software engineers building digital infrastructure and dashboards. often,” said Mike Kraus, founder and former director of an AI startup. Data science beyond limits.

The risks of limiting AI to the technology side include the ability of AI tools to adapt to new scenarios, alignment with enterprise objectives and goals, accuracy of responses due to data hallucinations, and ethical concerns including privacy. , said CEO Pari Natarajan. Zinov. But the risks go beyond data illusions and lack of nuanced understanding. AI efforts led solely by engineers risk optimizing for the wrong ends, are inconsistent with human values ​​such as empathy and compassion, lack checks and balances, and exacerbate bias. There is a possibility.

Everyone's goal should be to develop and ensure trustworthy AI, Thurman argues. This means creating systems and products that prioritize human well-being and help users understand how it works. For example, imagine a personal assistant that stores all your personal data locally, improves in functionality as you use it, and maintains your privacy. Or social media apps that can tweak their algorithms to improve your mental health instead of hurting it. These things are possible.

This means working closely with end users, customers, and others who rely on the output of these systems. Thurman says AI needs to be built to put humans in the driver's seat. This is paramount to avoid risks that are already occurring, such as consumers receiving incorrect medical advice from chatbots or algorithms sending tech job listings to men instead of women. .

Incorporating non-technical humanities talent into AI management teams should be organic and encouraged by culture, not forced by executive command. In other words, it's a balancing act, and for organizations with rigid, hierarchical cultures, it can be an uphill climb.

People inside and outside your organization should lead this effort. Dr. Bruce Lieberthal, vice president and chief innovation officer at Henry Schein, sees a risk of a lack of user collaboration within the healthcare sector. “Creators of the technology used by healthcare professionals and their patients often work within a black box and make decisions that do not adequately consider users,” he warns.

Lieberthal adds that AI is best developed collaboratively, and software teams need to meet with users and those most affected by the product to maintain integrity. Software should always be vetted by the same people to ensure it hits the mark on what users envisioned during development and deployment. ”

On the humanities side, Kraus says there are efforts to bring ethicists into the conversation at some level. However, it is often not clear what authority or role they actually play. He continued that the challenge of pushing philosophers into technology delivery teams is neither well received nor ultimately leads to positive outcomes for companies and users.

These humanities-oriented roles should be more advisory than actual decision-making, Kraus advises.

Still, he points out that a highly diverse AI team can help add a safeguard that can help organizations avoid headaches and wasted investments. Analyzing the problem from an ethical or philosophical perspective will raise many questions that a purely technical team probably wouldn't have asked, he says. More importantly, technical teams are not empowered, asked, or expected to think about the impact of what they are building, but rather focus on their mission and execute it. This means that it is expected. There are risks in not investigating unintended side effects of how AI is ultimately used.

The increasing democratization of AI thanks to services like GPT and Google will help open up AI decision-making to more diverse leadership. We must be wary of dilettantism and require interdisciplinary and interdisciplinary collaboration, says Natarajan. People with backgrounds in ethics, cognition, sociology, decision theory, and other humanities or social science disciplines, along with subject matter experts, can provide critical diversity to ground AI in practical, real-world situations. can provide a unique perspective.

The potential emergence of new roles means new maturity and caution in the integration of AI, Natarajan observes, learning from issues such as algorithmic bias. As AI grows more capable and autonomous, it can help maintain human oversight and control. These new roles could include chief AI ethics officer, AI ombudsman, AI compliance officer, AI auditor, and AI UX designer or curator.

Sources 1/ https://Google.com/ 2/ https://www.forbes.com/sites/joemckendrick/2024/04/27/ideal-ai-dream-team-technologists-strategists-users-and-ethicists/ The mention sources can contact us to remove/changing this article

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