Did I do any impact assessments? I probably did but nothing related to data protection or privacy. Did the solution cause any harm to anyone? I don’t think so. However, I remembered I spent a fair amount of time reflecting on the users of the tracing systems. The main users were those affected /impacted by the War and the administrators of the systems. Would any Data Protection Impact Assessments (DPIAs) and Privacy Impact Assessments (PIAs) method help me back in 1998?

As part of my PhD, I examined various DPIAs and PIAs and I can say I’m not any wiser in using those impact assessments method except that now I am aware of Data Protection, data privacy and Human Rights.

Right now, I’m reflecting on the contact tracing app after reading the article – The NHS Contact Tracing App: 11 key talking points –published by the BCS. I wonder whether any of the DPIAs or PIAs method has been used by the app designers. I suspect not.

I asked whether the WP29 thinking (& their opinions) about the 3 properties are for the ‘direct and indirect’ way of identification of the personal data. The answer was not in the method itself but that the properties are for ‘all data types’ i.e. any dataset. Her exact wordings ‘ use by anyone on any dataset’. Also, the DPA (DPO/Organisation?) needs to prove (or justify or show) that the dataset has indeed been anonymised (using any of the 2 options). My understanding is that the anonymisation if done (risk-based, database and/or algorithmic-driven) should not enable the direct and indirect re-identification of the individual(s).
As noted on this slide: ‘No single technique eliminates all risks’.

It’s near impossible to identify/isolate ‘all the direct/indirect re-identification risks’ associated with any dataset, assuming the dataset is available and not hidden in some Cloud and/or in a chain of hidden registers.

We really need to re-think personal data in terms of ‘the harm to individuals’ as there’s no absolutely sure way of preventing re-identification risks (i.e. singling out, linkability or inference/deduction etc.)

Overall a great talk.

I just noticed the slides and talk are available online: BCS Law talk 29th March 2018

The url:

https://twitter.com/datachainrisk/status/971197667660492800

What has been stated is that the review process is the most expansive aspect of edisc (i.e. expansive labor and time and voluminous document). So much so that predictive coding is now not only a tool used for the review process but also has become a topic of its own in the US courts. Is the review process also the most difficult of the edisc process? If you answer ‘yes’ or ‘no’, why do we need predictive coding?
Perhaps some answers (hopefully with relevant questions raised too) will appear in the new book Predictive Coding for Dummies book.

I wonder why there are no Dummies or Idiot’s Guide to the review process, instead one on predictive coding. I guess it is easier to write about a piece of tool/technique and not too easy to write on the review process or not profitable enough to do so.

I wonder whether the survey/research also considered Open Source ediscovery platforms. I suspect not from the free review at Law.com.

I also wonder whether the Magic Quadrant took into consideration the usage or the applicability of the products for edisclosure as in international arbitration and/or cross-border disputes. I suspect not.

Paper.li seems to use the twitter (or google+, facebook) handler/user to anchor the data stream and then allow tags/keywords to search/pull for the tagged content across handlers and their contents. Paper.li then sifts, sorts and aggregates (i.e. curate the cluttered web?) into the typical paper-style categories, with one interesting aspect i.e. allowing user-defined tagged-categories. The semantic web put into practise?

I have three twitter accounts, one for fun/social/friends, one for Alternative Dispute Resolution (ADR) stuff, and one for ediscovery/edisclosure (edisc) stuff. So far, my edisc twitter has the largest followers and the largest klout. Currently my tags are cyber, agile, databreach, and cyberinsurance. I wonder what my paper, The Data Chain, will reveal if I use tags on edisclosure and ediscovery. Since I have the largest followers on edisc stuff, I guess The Data Chain paper will be choked with edisc stuff. These tags and also twitter lists will be use in the next edition of The Data Chain.

Please note that I have not included the trademark symbol in this blog & in the blog’s title as I am not referring to Recommind’s patented  ‘predictive coding’.

Embracing technology, including predictive coding, is ultimately a game-changing  ‘war’ (Predictive Coding war breaks out in US ediscovery sector? ).

Let’s hope more predictive coding technology will be deployed for cost cutting the ediscovery costs and not be locked in competition mode.

This development is also interesting for the ediscovery space as we now have ‘microdata’ .

For my own record – schema.org

For information, the electronic discovery database is maintained by K&L Gates and here’s the blurb:

“Electronic Discovery Case Database  K&L Gates maintains and continually updates a database containing over 900 electronic discovery cases collected from state and federal jurisdictions around the United States.  This database is searchable by keyword, as well as by any combination of 28 different case attributes, e.g., on-site inspection, allegations of spoliation, motion for a preservation order, etc.  Each search will produce a list of relevant cases, including a brief description of the nature and disposition of each case, the electronic evidence involved and a link to a more detailed case summary if available.”