Artificial intelligence (AI) undoubtedly offers tremendous benefits across many industries, including medicine, agriculture, and transportation. However, there is growing evidence of potential for harm from AI as biased and inaccurate algorithms routinely make decisions on job applicants, healthcare, social services, parole and criminal justice, policing, and access to credit and insurance.

The AI researcher, Noel Sharkey, argues that “algorithms are so ‘infected with biases’ that their decision-making processes could not be fair or trusted” and that we should be “testing AI decision-making machines in the same way as new pharmaceutical drugs are vigorously checked before they are allowed on to the market”.

We have produced a report on the Responsible use of AI (RAI) to provide guidelines for organisations engaged in the development and deployment of AI-based applications, with particular reference to applications that involve algorithmic decision-making (ADM). The report develops:

• provides an introduction to AI and machine learning
• describes the AI development and deployment process for organisations
• describes how networks of algorithms can interact to produce AI harms
• defines responsibilities for AI subjects as well as for AI deploying organisations
• proposes a set of responsible AI principles
• maps risks to AI principles
• builds a wide-ranging set of recommendations for organisations using AI

We use the report in our teaching at UNSW and encourage others to consider it in their classes. Any comments or feedback on the report welcomed.

The link to the video “The Internet of Things changes the game for Hamburg Port Authority” is no longer available. The video can now be found here: https://www.youtube.com/watch?v=gYGyH5XZC3A

If you find any broken links, please let us know using the contact form.

The DataRobot software is now free to use for teaching: “DataRobot provides qualified and approved institutions of higher education with DataRobot licenses for student use so that they can explore advanced machine learning without programming in their coursework.”

DataRobot is a leading automated machine learning (AML) platform and is the subject of Chapter 11 of the textbook. DataRobot is perfect for analytics courses where you don’t want to have students (e.g., MBA) programming or getting heavily into the weeds of statistics and individual machine learning methods.

If you are a faculty member and would like to use DataRobot in your teaching, go here to sign up – https://www.datarobot.com/success/academic-support-program/

On 31 December 2020 the Flash plugin will no longer work in Chrome. SAS Visual Analytics (SAS VA) uses Flash and will therefore cease to work at the end of 2020. The replacement for SAS VA is SAS Viya. SAS offer a cloud-based version, Viya for Learners, that is free to use by educators and students. We recommend that anyone currently using SAS VA upgrade to SAS Viya.

Once in Viya, the contents of chapters 4 to 9 can be replicated using the Viya Report Viewer. In the coming months we will be updating the book to replace SAS Visual Analytics with Viya.

SAS have kindly loaded the datasets for our analytics textbook to their Viya for Leaners platform. We have produced a short guide to accessing the textbook data files from the Viya platform and replicating the use of SAS Visual Analytics.

Viya has many features and functionality that go beyond Visual Analytics, which we will incorporate in the next edition.

We have developed a practical approach to incorporating an ethical analysis into business analytics development projects. The research is reprted in:

Vidgen, R., Hindle, G., and Randolph, I., (2020). Exploring the ethical implications of business analytics with a business ethics canvas. European Journal of Operational Research, 281(3): 491-501

For an overview of the approach, see https://www.macmillanihe.com/blog/post/the-business-ethics-canvas-vidgen/

We’ve added a new case study, “Building an analytics capability at Barchester College”, to the instructor resources. The case has been developed in a similar style to the GoGet case in the book and is intended to be used in conjunction with chapters 14 (analytics methodology), 15 (design thinking), and 16 (ethics). While Barchester College is a pseudonym, the material in the case is drawn from interviews with a real-life organization. Having a case study based in higher education is particularly useful as it is a setting that students who have not worked in an organization prior to their studies will understand, e.g., the material on recruitment and student attrition. It is suitable for any of the exercises in the book where it is proposed that learners draw on “their current educational institution”. The case is accompanied by an instructor note.

The BACA survey is contained in Appendix C of the textbook and is available in Excel via the resources page. We have also implemented the BACA using the Qualtrics survey design software, as described here.

Try the survey

You can complete the BACA survey in Qualtrics for your own organisation. Once you have finished the survey you will see a summary of the results for all the survey responses received so far (also downloadable as a pdf). No identifying data is collected and thus both the organizations surveyed and the respondents are fully anonymous.

The Mango radar chart in Figure 2.7 of the book has been removed by the content owner. We recommend replacing this exercise with the ‘Data Scientist Personality Test‘, which has 10 questions. This survey is available in Qualtrics.

If data scientist profiling is being used for as part of group work then each team member should self-assess and see which cell of the data scientist matrix they are placed in. The team can then combine the individual profiles and see where they might want to bolster their skill set if they were to tackle a real-world business analytics project.