The chapter defines the concept of approach, along with its derivation and exemplification. A general definition of approach is proposed. The latter is then applied to HCI research in particular. An approach to HCI comprises the addressing of the topic or problem of human–computer interaction research; the performing of actions to progress the approach to the addressing of the topic or problem of human–computer interaction research; the evaluating of the success of the actions performed to progress an approach to the addressing of the topic or problem of human–computer interaction research; and the cumulating of the successes of whether the topic or problem of human–computer interaction research has been addressed or not. The definition is both explicit and sufficiently well specified for the later application of frameworks to the approaches, retained here – innovation, art, craft, applied, science and engineering.

The chapter assesses the General Framework for HCI research for completeness against other frameworks for HCI. The General Framework comprises concepts of discipline, general as common, general problem, particular scope, general research, general knowledge and general practices. These concepts are assessed for completeness against 10 individual other frameworks, which are in turn assessed for completeness against the General Framework. Overall, the General Framework is considered to be complete, although this depends much on the coherence of individual concepts. This assessment, however, does not constitute a validation of the General Framework. It is nevertheless indicative of its comparability with other HCI frameworks with respect to its completeness. The frameworks are further considered as concerns their dissemination in the HCI research literature. Dissemination is understood to include the framework’s disseminators, as well as the dissemination’s content, media and means. The General Framework is then assessed for its accommodation of the factors that appear to influence the success of framework dissemination.

The chapter presents the science approach to HCI research, including an illustration from the literature. The latter presents the case for developing new forms of psychology deep theory, based on generic systems of interactors. The chapter then presents the specific science framework for HCI research comprising science as discipline, general problem, particular scope, research, knowledge and practices. The specific science framework is followed by the science design research exemplar, as the science design cycle, the applied design research cycle and the science design research cycle. The lower-level science framework comprises the science application, the science interactive system, and the science interactive system performance. Both the exemplar and the lower-level framework are applied to the same illustration of the science approach, taken from the literature, which presents the case for developing new forms of psychology deep theory, based on generic systems of interactors.

We live in an algorithmic world. There is currently no area of our lives that has not been touched by computation and its language and tools. Since when, in the early 1940s, a small group of people led by John von Neumann gathered to turn into reality the vision of a universal computing machine, humankind is experiencing a sort of permanent revolution in which our understanding of the world and our ways of acting on it are steadily transformed by the steps forward we make in processing information. Such a condition is vividly depicted by Alan Turing in one of the founding documents of the quest for artificial intelligence (AI): “in attempting to construct machines … we are providing mansions for the souls.”1 Computers and algorithms can be seen as the building blocks of a new, ever-expanding building – a cathedral, to use George Dyson’s metaphor2 – in which every human activity is going to be shaped by the digital architecture hosting it.

Algorithms in society are both innocuous and ubiquitous. They seamlessly permeate both our on- and offline lives, quietly distilling the volumes of data each of us now creates. Today, algorithms determine the optimal way to produce and ship goods, the prices we pay for those goods, the money we can borrow, the people who teach our children, and the books and articles we read – reducing each activity to an actuarial risk or score. “If every algorithm suddenly stopped working,” Pedro Domingos hypothesized, “it would be the end of the world as we know it.”1

Public administration in Norway and in many other countries has used computers for more than fifty-five years. It is normal and necessary. Of course, it is possible to imagine many more office buildings where thousands of men and women would do all the detailed processing of individual cases that are processed today by computers, but this alternative is not very realistic: Modern taxation systems, national social insurance schemes and management of many other welfare programs would not be feasible without the use of computers and the algorithmic law that is integrated in the software. Thus, the question is not if public administration should apply computer technology, but how this should be done. This chapter deals with important how-to questions.

Transparency has been in the crosshairs of recent writing about accountable algorithms. Its critics argue that releasing data can be harmful, and releasing source code won’t be useful.1 They claim individualized explanations of artificial intelligence (AI) decisions don’t empower people, and instead distract from more effective ways of governing.2 While criticizing transparency’s efficacy with one breath, with the next they defang it, claiming corporate secrecy exceptions will prevent useful information from getting out.3

This chapter’s thesis is simple: as a general matter, agreements are a functional and conceptually straightforward way for the law to recognize algorithms. In particular, using agreements to recognize algorithms into the law is better than trying to use the law of agency to do so.1 Casual speech and conceptualism have led to the commonplace notion of “electronic agents,” but the law of agreements is a more functional entry point for algorithms to interact with the law than the concept of vicarious action. Algorithms need not involve any vicarious action, and most of the law of agency translates very poorly to algorithms that lack intent, reasonable understanding, and legal personality in their own right; instead, algorithms cause activity that may have contractual or other agreement-based legal significance. Recognizing the power (and perhaps the necessity) of addressing algorithms by means of the law governing agreements and other legal instruments can free us from formalistic attempts to shoehorn algorithms into a limited set of existing legal categories.

The United States’ transition from an economy built on form contracts to an economy built on algorithmic contracts has been as subtle as it has been thorough. In the form contract economy, many firms used standard order forms to make and receive orders. Firms purchased products and services with lengthy terms of services. Even negotiated agreements between fairly sophisticated businesses involved heavy incorporation of standard form terms selected by lawyers.

Online reputational injury can occur in a number of ways; and one way is through the use of algorithms that pervade the Internet. The Internet is comprised of complex technologies that enable the dissemination of information rapidly, providing global reach in a matter of seconds through the click of a button. The Internet provides robust public discourse over a gamut of topics in real-time and allows individuals from different parts of the world to interact with one another while preserving some sense of anonymity (that is, if they so choose). Many online communications stem from one piece of content that is regurgitated and redistributed on multiple platforms. For example, consider the social application Twitter. Twitter allows individuals to transmit bite-sized pieces of data among millions of users. Twitter has surprisingly become an outlet for a recent US President, and the fact that the public has direct access to a sitting President in this manner is undoubtedly incredible. Further, the Internet emboldens individuals to act behind the shield of a screen. There is little cost to spreading information, ideas, and gossip online, with seemingly few ramifications. Despite what may be perceived as a few keystrokes that have an ephemeral impact, content on the Internet has a tendency of permanence.

The use of “algorithms” in criminal investigation, adjudication, and punishment is not a new phenomenon. That is, to the extent that “algorithms” are simply sets of rules capable of being executed by a machine, the criminal justice system has long incorporated their use. For example, the US sentencing guidelines are so mechanistic that they were, at least for a time, literally calculated by software. Likewise, the New York Police Department’s erstwhile “stop and frisk” program was a mechanistic means of deciding whom to search and when. And so-called “per se” impaired driving laws have for nearly half a century mechanistically imposed criminal liability based on a machine’s determination that a person’s blood-alcohol level is over a certain threshold, without a jury determination of dangerous impairment.