Cutter Business Technology Journal — Calls for Papers
For nearly 30 years, the monthly Cutter Business Technology Journal has served as a forum for thought leaders in academia and industry to present innovative ideas and solutions to the critical issues facing business-technology professionals. Please consider sharing your insight with us for the following topics. For questions or to send article ideas, please contact Christine Generali at cgenerali[at]cutter[dot]com.
AI: Avoiding and Addressing Unintended Consequences
Guest Editor: Lou Mazzucchelli
Abstract Deadline: Aug 2, 2019
Article Deadline: September 6, 2019
I’ve been around long enough to see at least three AI waves - the first in the late 70s, when LISP machines emerged. I remember talking with Patrick Winston at Symbolics, who blithely remarked that, “in the future, every computer will be a LISP machine.” (He was right, but only because general-purpose architectures later got fast enough to run LISP at scale.)
I characterize this first wave as “our AI reach exceeded our hardware performance grasp” - many projects were tried, very few succeeded, and the first wave subsided.
This does not mean that AI disappeared, but it went offstage and people worked on it in the wings. I’ve discussed this phenomenon at length for years.
The second AI wave was ushered in by natural language recognition. Drastically increased CPU cost/performance allowed designers to create devices that do a reasonable job of dealing with voice in ever-less-restricted domain areas. This technology has been further enhanced by the harbinger of the third AI wave.
The third AI wave is being led by machine learning (ML). ML generates responses using directed pattern-matching and feedback to satisfy a goal, like “detect an edge” or “indicate this is a picture of a cat,” or “indicate this human is a felon.” These systems gain “skill” ingesting ever-larger data sets (“training”). We feed the systems ever-larger data sets to improve this training. We have observed some spectacular results (world class Go-playing systems “grown” in weeks, etc). However, we are at a loss to explain, at a micro level, how decisions in these systems are made.
In the human world, accepting a decision without questioning the facts leading up to it is a definition of “trust.” By that definition, we are placing a lot of trust in ML systems that are increasingly running the joint, from Google pet searches to hiring decisions. I see several problems emerging:
- Where is the record of initial training approaches for any ML system? We cannot be sure that bias has not been introduced into the system without knowing initial conditions and weights.
- Where is the record of changes in response to training input, and how is that input supplied or collected? Who gets to decide?
- Where does liability lie for ML mis-characterizations?
While we can laugh at stories about ML misidentifying US Congress members as criminals, life becomes more difficult for someone denied a job or promotion by an opaque ML-based system.
Perhaps the most dangerous aspect of the third wave of AI is its apparent efficacy. Putting a monster in a tuxedo may make it appear less threatening, but likely only masks other problems.
An upcoming issue of Cutter Business Technology Journal will explore ways to deal with problems of transparency, responsibility, and consequences for users and designers of new AI-based systems. (I suggest that getting ahead of these issues will be easier than dealing with negative consequences, while realizing that suggestion runs counter to human history).
Topics of discussion may include (but are not limited to) the following:
- How can the unintended consequences of AI be prevented?
- How can the fairness of algorithms be ensured? Is it even possible?
- How can organizations prepare themselves for AI fallout? What steps should be taken?
- What type of organizational governance is needed?
- How can the introduction of bias into a system be addressed/avoided?
- How can the transparency of AI algorithms be improved?
- How can you stop algorithms from being maniuplated?
- How can effective management help minimize the risks of negative consequences?
ARTICLE IDEAS. Please send article ideas to Lou Mazzucchelli and Christine Generali (email@example.com and firstname.lastname@example.org) including an abstract and short article outline showing major discussion points. Accepted articles due September 6, 2019. Final article length is typically 2,000-3,500 words plus graphics. More editorial guidelines.
Call for Papers: Is Software Eating the World?
Guest Editor: Greg Smith
Abstract Deadline: July 6, 2019
Article Deadline: August 3, 2019
I have been increasingly convinced in the last couple of years that Marc Andreessen’s “Why Software is Eating the World” essay is coming true. Combined with AI, the rise of software represents the biggest single challenge and opportunity to business.
Spurred by rapid-fire technological innovation, the present-day software revolution has not only propelled the growth of new software organizations; it has motivated traditional businesses, in pursuit of greater profitability, greater market share and increased enterprise value to transform into software organizations, as well.
What’s the play for established incumbents that have yet to digitally transform? How can they compete with rival software organizations who have the potential to disrupt (or have already disrupted) their market? What resources are needed to transform their operations and infrastructure to a digitally-enabled one?
Eric Ries defines a pivot as a change of strategy without a change of purpose; you can’t have a pivot without vision. This feels like a good way to think about the change needed for most businesses with respect to software. What type of leadership and strategic vision is needed to effect a successful transformation?
An upcoming issue of Cutter Business Technology Journal with Guest Editor Greg Smith seeks to address the complexities faced by today’s organizations competing in our software-driven world.
Topics of discussion may include (but are not limited to) the above issues as well as the following:
- What happens when your business has to become a software company?
- What happens if you are innovating in a physical system and your competitor is innovating in a software system?
- If innovation in your business is now software-driven, can you still outsource this to third parties?
- What are the measures and indicators that indicate whether a business is thriving or failing as a software-enabled organisation?
- What barriers might keep you from transforming your legacy systems successfully? How can these barriers be addressed?
- How does the leadership team need to change to reflect this? Who on the exec team understands this world and how do decision making processes get remade?
- What happens when you separate the software system from the physical activity system and it potentially becomes worth more than the legacy business?
ARTICLE IDEAS. Please send article ideas to Greg Smith and Christine Generali (email@example.com and Smith.Greg@adlittle.com) including an abstract and short article outline showing major discussion points. Accepted articles due August 3, 2019. Final article length is typically 2,000-3,500 words plus graphics. More editorial guidelines.
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1DeMarco, Tom, and Timothy Lister. Waltzing with Bears: Managing Risk on Software Projects. Dorset House, 2003.
2In this survey, “innovation” refers to any new initiatives to introduce innovative, leading-edge, or unconventional software project development methods, processes, tools, or techniques.
3Hall, Curt. “AI & Machine Learning in the Enterprise, Part XI: Success of AI Application Development Efforts.” Cutter Consortium Data Analytics & Digital Technologies, Executive Update, Vol. 19, No. 3, 2019.
4DeMarco and Lister (see 1).
5“Smart grid.” Wikipedia.