Is World Trade Organization Information Good Enough? How a Systematic Reflection by Members on Transparency Could Promote Institutional learning. Bertelsmann Stiftung GED Focus Paper

The World Trade Organization (WTO) has three primary tasks: to negotiate new rules, monitor implementation, and settle any disputes that arise. It is not fulfilling any of these tasks very well at the moment. Should Members just muddle along, hoping for the best, or seek external advice on how to change the WTO operating system? I suggest a third possibility: should Members encourage institutional learning? It helps that at least some Members know that they have a problem. In July 2017, a communication to the Gen-eral Council from a group of 47 developing and developed Members said, first, that the political will to find compromises and to forge consensus is lacking (WTO, 2017c). WTO Members have failed for years to agree that the Doha Round is dead so a new negotiation framework cannot be created. The second observation from the group informally known as the Friends of the System is that compliance with notification obligations is often unsatisfactory, thereby undermining the WTO’s monitoring function because information is late, incomplete or of low quality. These two self-criticisms, about political will to find consensus, and insufficient provision of infor-mation, are related: both are a symptom, not a cause; both signify the lack of a shared understanding of what WTO is for. Some of the explanations for the WTO’s difficulties lie outside the organization in a general malaise of multilater-alism (Wolfe, 2015b; Wolfe, 2017b), now exacerbated by an Administration in Washington that in putting America First sees a competitor, not a partner, in China, but continuing as before and hoping for a better outcome would be foolish. Muddling through is no solution, and outsiders cannot help. Could a systematic discussion of transpar-ency help Members to recover a shared sense of collective purpose

Reforming Public School Systems Through Sustained Union-Management Collaboration

Presents case studies of sustained collaboration between teachers' unions and management in school reform; common elements in initiating events, strategic priorities, supportive system infrastructure, and sustaining factors; and lessons learned

Data analytics and algorithms in policing in England and Wales: Towards a new policy framework

RUSI was commissioned by the Centre for Data Ethics and Innovation (CDEI) to conduct an independent study into the use of data analytics by police forces in England and Wales, with a focus on algorithmic bias. The primary purpose of the project is to inform CDEI’s review of bias in algorithmic decision-making, which is focusing on four sectors, including policing, and working towards a draft framework for the ethical development and deployment of data analytics tools for policing. This paper focuses on advanced algorithms used by the police to derive insights, inform operational decision-making or make predictions. Biometric technology, including live facial recognition, DNA analysis and fingerprint matching, are outside the direct scope of this study, as are covert surveillance capabilities and digital forensics technology, such as mobile phone data extraction and computer forensics. However, because many of the policy issues discussed in this paper stem from general underlying data protection and human rights frameworks, these issues will also be relevant to other police technologies, and their use must be considered in parallel to the tools examined in this paper. The project involved engaging closely with senior police officers, government officials, academics, legal experts, regulatory and oversight bodies and civil society organisations. Sixty nine participants took part in the research in the form of semi-structured interviews, focus groups and roundtable discussions. The project has revealed widespread concern across the UK law enforcement community regarding the lack of official national guidance for the use of algorithms in policing, with respondents suggesting that this gap should be addressed as a matter of urgency. Any future policy framework should be principles-based and complement existing police guidance in a ‘tech-agnostic’ way. Rather than establishing prescriptive rules and standards for different data technologies, the framework should establish standardised processes to ensure that data analytics projects follow recommended routes for the empirical evaluation of algorithms within their operational context and evaluate the project against legal requirements and ethical standards. The new guidance should focus on ensuring multi-disciplinary legal, ethical and operational input from the outset of a police technology project; a standard process for model development, testing and evaluation; a clear focus on the human–machine interaction and the ultimate interventions a data driven process may inform; and ongoing tracking and mitigation of discrimination risk

Exploring the remuneration ‘black box’: establishing an organizational learning insight into changing remuneration committee ‘social worlds’

Current executive compensation research posits a need to extend analysis beyond principalagent theory in order to explore the complex social influences and processes implicated in Remuneration Committee (RemCo) decision-making (e.g. Bender, 2007; Kakabadse et al, 2006; Main et al., 2007), particularly given the current uproar surrounding reported levels and structuring of executive remuneration. We respond to this international need by highlighting how innovative organizational learning theorizing can be integrated into further investigations of the remuneration ‘Black Box’, in order to focus attention upon the nuances of what and how organizational learning takes place in the remuneration process. Additionally, we note the importance of investigating the main actors and particularly their performance of complex roles within their rapidly evolving ‘social worlds’. By exploring the organizational learning phenomena implicated in executive remuneration, we argue that practitioners, regulatory bodies etc. can appreciate further the implications of their respective decision-making