Letters to the editor

Embed Ethical Guidelines in Autonomous Weapons

  1. Introduction
  2. When the Web Arrived for Me
  3. How to Really Encourage Women in Computing
  4. Author Responds:
  5. Causal Connections for Predictive AI
  6. Correction
  7. References
  8. Footnotes
Letters to the Editor, illustration

As a combat veteran and more recently an industry technologist and university professor, I have observed with concern the increasing automation—and dehumanization—of warfare. Sarah Underwood’s discussion of autonomous weapons in her news story "Potential and Peril" (June 2017) highlighting this trend also reminded me of the current effort to update the ACM Code of Ethics, which says nothing about the responsibilities of ACM members in defense industries building the software and hardware in weapons systems. Underwood said understanding the limitations, dangers, and potential of autonomous and other warfare technologies must be a priority for those designing such systems in order to minimize the "collateral damage" of civilian casualties and property/infrastructure destruction.

Defense technologists must be aware of and follow appropriate ethical guidelines for creating and managing automated weapons systems of any kind. Removing human control and moral reasoning from weapons will not make wars less likely or less harmful to humans.

Harry J. Foxwell, Fairfax, VA

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When the Web Arrived for Me

Regarding Neil Savage’s excellent news story on Tim Berners-Lee, "Weaving the Web," and Leah Hoffman’s likewise excellent Q&A with Berners-Lee, "This Is for Everyone," (both June 2017), I would also add that the cost of computing has come down so much that organizations today are able (if willing) to allow outsiders to use their CPU resources. This would have been unthinkable in the mid-1980s. A computing colleague later suggested a routine based on the Bob Newhart-humor model, characterized by a phone call with a skeptical boss regarding new products or technologies. The audience would usually hear only the boss’s side of the conversation; see, for example, the "Bob Newhart Tobacco video (Sir Walter Raleigh phone conversation)" at https://www.youtube.com/watch?v=_XDxAzVEbN4. Consider Tim Berners-Lee or other World Wide Web evangelist trying to convince his boss that letting the whole world tap the organization’s expensive and secure CPU cycles to support the Web for the benefit of all humanity would indeed be a good idea. As with Newhart, we would imagine hearing it from the boss’s perspective. For me, I knew Berners-Lee was onto something when only a month or so after I began experimenting with Mosaic and Netscape, I saw http://www.coca-cola.com printed on a can of Coke.

W. Terry Hardgrave, Cross Junction, VA
ACM Member since 1967

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How to Really Encourage Women in Computing

Reflecting on my involvement in the software profession in the 1950s and 1960s, when male and female participation were roughly equal, I can totally agree with Valarie Barr’s view in her "From the ACM W-Chair" column "Gender Diversity in Computing: Are We Making Any Progress?" (Apr. 2017) and am astonished how few women enter or stay in the field 60-odd years later. Barr wrote that women are "hemorrhaging out the side and back doors" of the field after five years. The women I knew who headed out those doors did so because they became mothers, so I think it important to compare the female dropout rate among software professionals with similar dropout rates in other fields. I suspect, at least in earlier times, they would be similar, though I am less certain about those rates today. Nevertheless, this kind of data is vital for understanding what is happening. Also, Barr mentioned ACM financial support for female computer science students "to attend research conferences." But attending research conferences and the longevity of practitioner employment have almost nothing in common. Surely ACM’s "encouragement" money could be spent in more direct and effective ways.

Robert L. Glass, Toowong, Australia

"Studies show engineering has the highest turnover rate for women compared to, say, accounting, law, and medicine, though the majority continues working."

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Author Responds:

There is no data about women’s persistence in scientific and engineering fields, including computer science, in the 1950s and 1960s, and we cannot draw conclusions based on anecdotal information alone. Current studies show engineering today has the highest turnover rate for women when compared to, say, accounting, law, and medicine, though the vast majority continues working; only 22% who have left report they are now doing "family care." Other research shows engineering and science culture is a much more significant factor in women’s lack of persistence than are family concerns. While attendance at research conferences may not translate directly into long-term practitioner employment, conference attendance does help sustain and increase excitement about staying in computer science.

Valerie Barr, South Hadley, MA

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Causal Connections for Predictive AI

As part of the "ACM Panels in Print" section "Artificial Intelligence" (Feb. 2017), panelist David Blei said he believes computer science needs to identify the causal connections between data components, concluding that artificial intelligence, with its predictive capabilities, will be enhanced through causal inference. For example, the first step toward using AI in database systems is to analyze and create a data map of the complexity of the causal interconnections between the data components in a problem space. A data map connects a data component to other data components through causal interrelationships. A data map can be created through qualitative analysis of data collected for a particular problem. The qualitative analysis could then take the form of "thematic analysis,"1 using systems diagramming to gain greater insight into the data. At that point it might be advantageous to start applying AI directly to the data.

Researching the complexity of database systems, I have thus created such a data map, which is now ready to move to the next stage of automation where predictive analytics can help improve management of database systems. Using an analogy of the CODEX, or Control of Data Expediently, my research into causal connections has identified a potential role for AI in automating continuously changing best practice, thus representing an agile approach to deciphering the complexity of interconnections and promising to help create an autonomous way to deliver best practice in database management.

Victoria Holt, Bath, U.K.

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In the ACM Member News column (May 2017), Dragomir Radev was mistakenly identified as a professor at the University of Michigan. Radev teaches at Yale University, where he also leads the Language, Information, and Learning at Yale (LILY) lab.

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