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AI, Treaty Bodies & the UPR

Using artificial intelligence and machine learning algorithms to support states to meet international obligations

In October, Women@theTable will host a briefing session with scientists from EPFL and MIT for states, civil society and others to explore the use of automated processes as a tool for assisting states in meeting their UPR and treaty body commitments and obligations and ultimately contribute to the realisation of the 2030 Agenda for Sustainable Development and the Sustainable Development Goals (SDGs).

 

The briefing session will outline how data science can be used to monitor UPR and treaty body recommendations / concluding observations and will provide practical case studies and research on how text mining and machine learning can be applied to track UPR and / or treaty body recommendations. Further details on this briefing session will be published on Women@theTable’s website shortly.

In October, Women@theTable will host a briefing session with scientists from EPFL and MIT for states, civil society and others to explore the use of automated processes as a tool for assisting states in meeting their UPR and treaty body commitments and obligations and ultimately contribute to the realisation of the 2030 Agenda for Sustainable Development and the Sustainable Development Goals (SDGs).

The briefing session will outline how data science can be used to monitor UPR and treaty body recommendations / concluding observations and will provide practical case studies and research on how text mining and machine learning can be applied to track UPR and / or treaty body recommendations. Further details on this briefing session will be published on Women@theTable’s website shortly.

It is clear that States have the primary responsibility for implementation of UPR and treaty body recommendations – this characteristic lies at the heart of the success and credibility of the UPR and treaty body processes.

Co-operative and self-reporting aspects of the UPR and treaty body reviews (in addition to the peer-to-peer aspect of the UPR) are strong contributors to the UPR mechanism’s success, they also create incentives for states to emphasise successes and acknowledge difficulties in their reporting on implementation of recommendations.

States must pay continuous attention to recommendations by all mechanisms including concluding observations / recommendations by treaty bodies, which often tend to overlap and complement UPR recommendations. Effectively monitoring hundreds of recommendations is daunting. Mechanisms to help States to streamline the process of implementation and reduce the reporting burden is needed.

In an effort to improve states’ accountability to the UPR and treaty bodies as well as support states monitor UPR recommendations and treaty body concluding observations, various initiatives have been developed to strengthen the monitoring processes. Implementation tracking tools have been developed in a number of countries across the globe, for example in India and in the Pacific Islands. The emergence over the past few years of ‘National Mechanisms for Implementation, Reporting and Follow-Up’ (NMIRFs) has been a step in the right direction, given these bodies usually enjoy high-level political backing and are responsible for the implementation of recommendations received by a State from all relevant UN human rights mechanisms.

However, this is not the complete picture. There is a vital role for civil society and National Human Rights Institutions (NHRIs) in this process, including as partners in implementation and to support states in robust monitoring and reporting processes, so states can meet international obligations and commitments made.

To date, no systemic methodology has been created to provide metrics to efficiently monitor the level of implementation of recommendations. One of the most promising yet least-understood developments is the ability to automate processes for tracking the progress of UPR and treaty body recommendations. Women@theTable is partnering with one of the global leaders in technology, Data-Pop Alliance, to explore how leveraging text mining and machine-learning algorithms is a viable strategy for monitoring UPR and treaty body recommendations. The partnership focuses on how the development of algorithms for analyzing and classifying, and proposing metrics can be utilised to assist states in monitoring UPR recommendations and treaty body concluding observations / recommendations

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