12. Sustainability

Favour implementations that effectively predict future behaviour and generate beneficial insights over a reasonable period of time.
Principle: A compilation of existing AI ethical principles (Annex A), Jan 21, 2020

Published by Personal Data Protection Commission (PDPC), Singapore

Related Principles

(Preamble)

Automated decision making algorithms are now used throughout industry and government, underpinning many processes from dynamic pricing to employment practices to criminal sentencing. Given that such algorithmically informed decisions have the potential for significant societal impact, the goal of this document is to help developers and product managers design and implement algorithmic systems in publicly accountable ways. Accountability in this context includes an obligation to report, explain, or justify algorithmic decision making as well as mitigate any negative social impacts or potential harms. We begin by outlining five equally important guiding principles that follow from this premise: Algorithms and the data that drive them are designed and created by people There is always a human ultimately responsible for decisions made or informed by an algorithm. "The algorithm did it" is not an acceptable excuse if algorithmic systems make mistakes or have undesired consequences, including from machine learning processes.

Published by Fairness, Accountability, and Transparency in Machine Learning (FAT/ML) in Principles for Accountable Algorithms, Jul 22, 2016 (unconfirmed)

(Preamble)

Google aspires to create technologies that solve important problems and help people in their daily lives. We are optimistic about the incredible potential for AI and other advanced technologies to empower people, widely benefit current and future generations, and work for the common good. We believe that these technologies will promote innovation and further our mission to organize the world’s information and make it universally accessible and useful. We recognize that these same technologies also raise important challenges that we need to address clearly, thoughtfully, and affirmatively. These principles set out our commitment to develop technology responsibly and establish specific application areas we will not pursue.

Published by Google in Artificial Intelligence at Google: Our Principles, Jun 7, 2018

· 1. Be socially beneficial.

The expanded reach of new technologies increasingly touches society as a whole. Advances in AI will have transformative impacts in a wide range of fields, including healthcare, security, energy, transportation, manufacturing, and entertainment. As we consider potential development and uses of AI technologies, we will take into account a broad range of social and economic factors, and will proceed where we believe that the overall likely benefits substantially exceed the foreseeable risks and downsides. AI also enhances our ability to understand the meaning of content at scale. We will strive to make high quality and accurate information readily available using AI, while continuing to respect cultural, social, and legal norms in the countries where we operate. And we will continue to thoughtfully evaluate when to make our technologies available on a non commercial basis.

Published by Google in Artificial Intelligence at Google: Our Principles, Jun 7, 2018

2. Transparency

For cognitive systems to fulfill their world changing potential, it is vital that people have confidence in their recommendations, judgments and uses. Therefore, the IBM company will make clear: When and for what purposes AI is being applied in the cognitive solutions we develop and deploy. The major sources of data and expertise that inform the insights of cognitive solutions, as well as the methods used to train those systems and solutions. The principle that clients own their own business models and intellectual property and that they can use AI and cognitive systems to enhance the advantages they have built, often through years of experience. We will work with our clients to protect their data and insights, and will encourage our clients, partners and industry colleagues to adopt similar practices.

Published by IBM in Principles for the Cognitive Era, Jan 17, 2017

· 2. (Humans will need) Education

Some argue that because lifespans will increase, birth rates will decline, and thus spending on education will decline. But I believe that to create and manage innovations we cannot fathom today, we will need increased investment in education to attain higher level thinking and more equitable education outcomes. Developing the knowledge and skills needed to implement new technologies on a large scale is a difficult social problem that takes a long time to resolve. There is a direct connection between innovation, skills, wages, and wealth. The power loom was invented in 1810 but took 35 years to transform the clothing industry because there were not sufficient trained mechanics to meet demand.

Published by Satya Nadella, CEO of Microsoft in 10 AI rules, Jun 28, 2016