Monday, October 4, 2021

g-f(2)537 THE BIG PICTURE OF THE DIGITAL AGE (10/4/2021), MIT SMR, EXECUTIVE GUIDE, Developing an Ethical Technology Mindset

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"g-f" fishing of golden knowledge (GK) of the fabulous treasure of the digital ageDigital Transformation, Developing an Ethical Technology Mindset (10/4/2021)  g-f(2)426 


EXECUTIVE GUIDE, Developing an Ethical Technology Mindset, MIT SMR

    • The demands of a digitized workforce put transparency, ethics, and fairness at the top of executive agendas. This MIT SMR Executive Guide explores how managers and organizations can apply principles of trustworthy and ethical technology in engaging with customers, employees, and other stakeholders.
    • Why Building an Ethical Culture Must Start at the Top. Integrity must be a priority for leadership if it’s to be taken seriously companywide.
    • Why ‘Explicit Uncertainty’ Matters for the Future of Ethical Technology. This new model would help by instituting explicit uncertainty as a norm. This moves companies and leaders to think through decisions and business-model choices with more long-term thinking around users and potential unintended consequences.
    • Designing Ethical Technology Requires Systems for Anticipation and Resilience. To avoid ethical lapses, organizations need to build systems that help to protect against preventable errors and to recover from ones that are unforeseeable.
    • Putting Dignity at the Core of Employee Data Use. When companies manage employee data responsibly, they’re better able to grow trust while gaining insights.
    • Design Your Work Environment to Manage Unintended Tech Consequences. Collaborative technologies enable many benefits for organizations in a time of widespread remote work, but they also come with risks of isolation, exclusion, surveillance, and self-censorship.
    • A New Business Mandate for Ethical Technology. Technology has become central to how all companies — not just tech companies — run their businesses and compete in the 21st century. At the same time, the pace of change, increased uncertainty, and the rising demand on organizations to address societal challenges have pushed the issue of ethical and trustworthy technology to the top of the executive agenda.

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              EXECUTIVE GUIDE, Developing an Ethical Technology Mindset, Various articles and authors, Articles from September and October 2021 until now, MIT Sloan Management Review, MIT SMR.

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              Opportunity, Why Building an Ethical Culture Must Start at the Top, MIT SMR

              • Ask corporate leaders what values drive their organizational culture, and they’ll likely cite integrity — or a closely related principle — near the top of the list. But as Robert Chesnut points out in his book Intentional Integrity: How Smart Companies Can Lead an Ethical Revolution, such assertions can often ring hollow. After all, companies like Barclays, Wells Fargo, and Volkswagen claimed to value integrity while nonetheless allowing major ethical lapses that resulted in scandal.
              • Robert Chesnut: For most companies, integrity has to get off of the corporate website and into the actual language of the leaders. The issue with integrity is that companies and leaders think they have it because they’re in favor of it. Everyone will tell you how important it is. But in reality, very few leaders actually speak about it in everyday work. And that’s a problem.
                • You have to start by defining your company’s North Star — its purpose. How you measure integrity has to connect back to that idea. This means that integrity can mean different things to different people in different companies, and that’s OK. You and I can probably agree that lying, cheating, and stealing lack integrity. But a lot of issues that arise are more nuanced than that. This is the challenging part. The value of intentional integrity is making these kinds of discussions part of the language of the company.

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              Opportunity, Why ‘Explicit Uncertainty’ Matters for the Future of Ethical Technology, MIT SMR

              • The biggest concerns over AI today are not about dystopian visions of robot overlords controlling humanity. Instead, they’re about machines turbocharging bad human behavior. Social media algorithms are one of the most prominent examples.
              • Algorithms like YouTube’s recommendation engine are programmed with an end goal: engagement. Here, machine learning adapts and optimizes based on user behavior to accomplish that goal. If certain content spurs higher engagement, the algorithm may naturally recommend that same content to other people, all in service of that goal.
              • This can have far-ranging effects for society. As Sen. Chris Coons of Delaware put it in April 2021 when executives from YouTube, Facebook, and Twitter were testifying before Congress, “These algorithms are amplifying misinformation, feeding political polarization, and making us more distracted and isolated.”
              • To address this issue, companies and leaders must consider the ethical implications of technology-driven business models. In the example of social media, how differently might an algorithm work if it instead had no end goal?
              • We can make AI more agile and responsive to individuals’ needs and subject to their choices. With algorithms increasingly powering our world, it’s time to change course.


              Opportunity, Designing Ethical Technology Requires Systems for Anticipation and Resilience, MIT SMR

              • Together, the increased speed and scale of emerging technologies can make ethical lapses more likely, more costly, and harder to recover from. To reduce ethical lapses, organizations need two kinds of systems: systems for anticipation and systems for resilience. In this article, we will examine frameworks for introducing these systems in organizations. We will draw on AI and data-driven examples from the real world, but these principles can be applied to a variety of emerging technologies.
              • The use of new technology by organizations involves both promise and perils. To realize more of the promise and minimize the perils, organizational leaders need to ensure the presence and robustness of systems for anticipation and resilience. The more we can catch mistakes early on that we are not currently sensing and anticipating, the better we can prepare to bounce back from mistakes that we cannot foresee.
              • One way to think about developing and implementing AI is that it is similar to developing policy at scale. A policy is a set of rules that affects millions of people in ways we might not be able to foresee.


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