# Misleading Metrics and Irrelevant Research (Accuracy and F1)

If one algorithm achieved 98.2% accuracy while another had 98.6% for the same task, would you be surprised to find that the first algorithm required ten times as much document review to reach 75% recall compared to the second algorithm?  This article explains why some performance metrics don’t give an accurate view of performance for ediscovery purposes, and why that makes a lot of research utilizing such metrics irrelevant for ediscovery.

The key performance metrics for ediscovery are precision and recall.  Recall, R, is the percentage of all relevant documents that have been found.  High recall is critical to defensibility.  Precision, P, is the percentage of documents predicted to be relevant that actually are relevant.  High precision is desirable to avoid wasting time reviewing non-relevant documents (if documents will be reviewed to confirm relevance and check for privilege before production).  In other words, precision is related to cost.  Specifically, 1/P is the average number of documents you’ll have to review per relevant document found.  When using technology-assisted review (predictive coding), documents can be sorted by relevance score and you can choose any point in the sorted list and compute the recall and precision that would be achieved by treating documents above that point as being predicted to be relevant.  One can plot a precision-recall curve by doing precision and recall calculations at various points in the sorted document list.

The precision-recall curve to the right compares two different classification algorithms applied to the same task.  To do a sensible comparison, we should compare precision values at the same level of recall.  In other words, we should compare the cost of reaching equally good (same recall) productions.  Furthermore, the recall level where the algorithms are compared should be one that is sensible for for ediscovery — achieving high precision at a recall level a court wouldn’t accept isn’t very useful.  If we compare the two algorithms at R=75%, 1-NN has P=6.6% and 40-NN has P=70.4%.  In other words, if you sort by relevance score with the two algorithms and review documents from top down until 75% of the relevant documents are found, you would review 15.2 documents per relevant document found with 1-NN and 1.4 documents per relevant document found with 40-NN.  The 1-NN algorithm would require over ten times as much document review as 40-NN.  1-NN has been used in some popular TAR systems.  I explained why it performs so badly in a previous article.

There are many other performance metrics, but they can be written as a mixture of precision and recall (see Chapter 7 of the current draft of my book).  Anything that is a mixture of precision and recall should raise an eyebrow — how can you mix together two fundamentally different things (defensibility and cost) into a single number and get a useful result?  Such metrics imply a trade-off between defensibility and cost that is not based on reality.  Research papers that aren’t focused on ediscovery often use such performance measures and compare algorithms without worrying about whether they are achieving the same recall, or whether the recall is high enough to be considered sufficient for ediscovery.  Thus, many conclusions about algorithm effectiveness simply aren’t applicable for ediscovery because they aren’t based on relevant metrics.

One popular metric is accuracy, which is the percentage of predictions that are correct.  If a system predicts that none of the documents are relevant and prevalence is 10% (meaning 10% of the documents are relevant), it will have 90% accuracy because its predictions were correct for all of the non-relevant documents.  If prevalence is 1%, a system that predicts none of the documents are relevant achieves 99% accuracy.  Such incredibly high numbers for algorithms that fail to find anything!  When prevalence is low, as it often is in ediscovery, accuracy makes everything look like it performs well, including algorithms like 1-NN that can be a disaster at high recall.  The graph to the right shows the accuracy-recall curve that corresponds to the earlier precision-recall curve (prevalence is 2.633% in this case), showing that it is easy to achieve high accuracy with a poor algorithm by evaluating it at a low recall level that would not be acceptable for ediscovery.  The maximum accuracy achieved by 1-NN in this case was 98.2% and the max for 40-NN was 98.6%.  In case you are curious, the relationship between accuracy, precision, and recall is:
$ACC = 1 - \rho (1 - R) - \rho R (1 - P) / P$
where $\rho$ is the prevalence.

Another popular metric is the F1 score.  I’ve criticized its use in ediscovery before.  The relationship to precision and recall is:
$F_1 = 2 P R / (P + R)$
The F1 score lies between the precision and the recall, and is closer to the smaller of the two.  As far as F1 is concerned, 30% recall with 90% precision is just as good as 90% recall with 30% precision (both give F1 = 0.45) even though the former probably wouldn’t be accepted by a court and the latter would.   F1 cannot be large at small recall, unlike accuracy, but it can be moderately high at modest recall, making it possible to achieve a decent F1 score even if performance is disastrously bad at the high recall levels demanded by ediscovery.  The graph to the right shows that 1-NN manages to achieve a maximum F1 of 0.64, which seems pretty good compared to the 0.73 achieved by 40-NN, giving no hint that 1-NN requires ten times as much review to achieve 75% recall in this example.

Hopefully this article has convinced you that it is important for research papers to use the right metric, specifically precision (or review effort) at high recall, when making algorithm comparisons that are useful for ediscovery.

# TAR vs. Keyword Search Challenge, Round 5

The audience was challenged to construct a keyword search query that is more effective than technology-assisted review (TAR) at IG3 West 2018.  The procedure was the same as round 4, so I won’t repeat the details here.  The audience was small this time and we only got one query submission for each topic.  The submission for the law topic used AND to join the keywords together and matched no articles, so I changed the ANDs to ORs before evaluating it.  The results and queries are below.  TAR beat the keyword searches by a huge margin this time.

 Biology Recall Query Top 3,000 Top 6,000 Search 20.1% 20.1% TAR 3.0 SAL 72.5% 91.0% TAR 3.0 CAL 75.5% 93.0%
 Medical Recall Query Top 3,000 Top 6,000 Search 28.5% 38.1% TAR 3.0 SAL 67.3% 83.7% TAR 3.0 CAL 80.7% 88.5%
 Law Recall Query Top 3,000 Top 6,000 Search 5.5% 9.4% TAR 3.0 SAL 63.5% 82.3% TAR 3.0 CAL 77.8% 87.8%

biology query: (Evolution OR develop) AND (Darwin OR bird OR cell)
medical query: Human OR body OR medicine OR insurance OR license OR doctor OR patient
law query: securities OR conspiracy OR RICO OR insider

# Highlights from IG3 West 2018

The IG3 West conference was held by Ing3nious at the Paséa Hotel & Spa in Huntington Beach, California. This conference differed from other recent Ing3nious events in several ways.  It was two days of presentations instead of one.  There were three simultaneous panels instead of two.  Between panels there were sometimes three simultaneous vendor technology demos.  There was an exhibit hall with over forty vendor tables.  Due to the different format, I was only able to attend about a third of the presentations.  My notes are below.  You can find my full set of photos here.

Stop Chasing Horses, Start Building Fences: How Real-Time Technologies Change the Game of Compliance and Governance

AI and the Corporate Law Department of the Future
Gartner says AI is at the peak of inflated expectations and a trough of disillusionment will follow.  Expect to be able to buy autonomous vehicles by 2023.  The economic downturn of 2008 caused law firms to start using metrics.  Legal will take a long time to adopt AI — managing partners still have assistants print stuff out.  Embracing AI puts a firm ahead of its competitors.  Ethical obligations are also an impediment to adoption of technology, since lawyers are concerned about understanding the result.

Advanced TAR Considerations: A 500 Level Crash Course
Continuous Active Learning (CAL), also called TAR 2.0, can adapt to shifts in the concept of relevance that may occur during the review.  There doesn’t seem to be much difference in the efficiency of SVM vs logistic regression when they are applied to the same task.  There can be a big efficiency difference between different tasks.  TAR 1.0 requires a subject-matter expert for training, but senior attorneys are not always readily available.  With TAR 1.0 you may be concerned that you will be required to disclose the training set (including non-responsive documents), but with TAR 2.0 there is case law that supports that being unnecessary [I’ve seen the argument that the production itself is the training set, but that neglects the non-responsive documents that were reviewed (and used for training) but not produced.  On the other hand, if you are taking about disclosing just the seed set that was used to start the process, that can be a single document and it has very little impact on the result.].  Case law can be found at predictivecoding.com, which is updated at the end of each year.  TAR needs text, not image data.  Sometimes keywords are good enough.  When it comes to government investigations, many agencies (FTC, DOJ) use/accept TAR.  It really depends on the individual investigator, though, and you can’t fight their decision (the investigator is the judge).  Don’t use TAR for government investigations without disclosing that you are doing so.  TAR can have trouble if there are documents having high conceptual similarity where some are relevant and some aren’t.  Should you tell opposing counsel that you’re using TAR?  Usually, but it depends on the situation.  When the situation is symmetrical, both sides tend to be reasonable.  When it is asymmetrical, the side with very little data may try to make things expensive for the other side, so say something like “both sides may use advanced technology to produce documents” and don’t give more detail than that (e.g., how TAR will be trained, who will do the training, etc.) or you may invite problems.  Disclosing the use of TAR up front and getting agreement may avoid problems later.  Be careful about “untrainable documents” (documents containing too little text) — separate them out, and maybe use meta data or file type to help analyze them.  Elusion testing can be used to make sure too many relevant documents weren’t missed.  One panelist said 384 documents could be sampled from the elusion set, though that may sometimes not be enough.  [I have to eat some crow here.  I raised my hand and pointed out that the margin of error for the elusion has to be divided by the prevalence to get the margin of error for the recall, which is correct.  I went on to say that with a sample of 384 giving ±5% for the elusion you would have ±50% for the recall if prevalence was 10%, making the measurement worthless.  The mistake is that while a sample of 384 technically implies a worst case of ±5% for the margin of error for elusion, it’s not realistic for the margin of error to be that bad for elusion because ±5% would occur if elusion was near 50%, but elusion is typically very small (smaller than the prevalence), causing the margin of error for the elusion to be significantly less than ±5%.  The correct margin of error for the recall from an elusion sample of 384 documents would be ±13% if the prevalence is 10%, and ±40% if the prevalence is 1%.  So, if prevalence is around 10% an elusion sample of 384 isn’t completely worthless (though it is much worse than the ±5% we usually aim for), but if prevalence is much lower than that it would be].

40 Years in 30 Minutes: The Background to Some of the Interesting Issues we Face

Digging Into TAR
I moderated this panel, so I didn’t take notes.  We did the TAR vs. Keyword Search Challenge again.  The results are available here.

After the Incident: Investigating and Responding to a Data Breach

Employing Technology/Next-Gen Tools to Reduce eDiscovery Spend
Have a process, but also think about what you are doing and the specifics of the case.  Restrict the date range if possible.  Reuse the results when you have overlapping cases (e.g., privilege review).  Don’t just look at docs/hour when monitoring the review.  Look at accuracy and get feedback about what they are finding.  CAL tends to result in doing too much document review (want to stop at 75% recall but end up hitting 89%).  Using a tool to do redactions will give false positives, so you need manual QC of the result.  When replacing a patient ID with a consistent anonymized identifier, you can’t just transform the ID because that could be inverted, resulting in a HIPAA violation.

eDiscovery for the Rest of us
What are ediscovery considerations for relatively small data sets?  During meet and confer, try to cooperate.  Judges hate ediscovery disputes.  Let the paralegals hash out the details — attorneys don’t really care about the details as long as it works.  Remote collection can avoid travel costs and hourly fees while keeping strangers out of the client’s office.  The biggest thing they look for from vendors is cost.  Need a certain volume of data for TAR to be practical.  Email threading can be used at any size.

Does Compliance Stifle or Spark Innovation?
Startups tend to be full of people fleeing big corporations to get away from compliance requirements. If you do compliance well, that can be an advantage over competitors.  Look at it as protecting the longevity of the business (protecting reputation, etc.).  At the DoD, compliance stifles innovation, but it creates a barrier against bad guys.  They have thousands of attacks per day and are about 8 years behind normal innovation.  Gray crimes are a area for innovation — examples include manipulation (influencing elections) and tanking a stock IPO by faking a poisoning.  Hospitals and law firms tend to pay, so they are prime targets for ransomware.

Panels That I Couldn’t Attend:
California and EU Privacy Compliance
What it all Comes Down to – Enterprise Cybersecurity Governance
Selecting eDiscovery Platforms and Vendors
Defensible Disposition of Data
Biometrics and the Evolving Legal Landscape
Storytelling in the Age of eDiscovery
Technology Solution Update From Corporate, Law Firm and Service Provider Perspective
The Internet of Things and Everything as a Service – the Convergence of Security, Privacy and Product Liability
Similarities and Differences Between the GDPR and the New California Consumer Privacy Act – Similar Enough?
The Impact of the Internet of Things on eDiscovery
Escalating Cyber Risk From the IT Department to the Boardroom
So you Weren’t Quite Ready for GDPR?
Security vs. Compliance and Why Legal Frameworks Fall Short to Improve Information Security
How to Clean up Files for Governance and GDPR
Deception, Active Defense and Offensive Security…How to Fight Back Without Breaking the Law?
Information Governance – Separating the “Junk” from the “Jewels”
What are Big Law Firms Saying About Their LegalTech Adoption Opportunities and Challenges?
Cyber and Data Security for the GC: How to Stay out of Headlines and Crosshairs

# Highlights from Text Analytics Forum 2018

Text Analytics Forum is part of KMWorld.  It was held on November 7-8 at the JW Marriott in D.C..  Attendees went to the large KMWorld keynotes in the morning and had two parallel text analytics tracks for the remainder of the day.  There was a technical track and an applications track.  Most of the slides are available here.  My photos, including photos of some slides that caught my attention or were not available on the website, are available here.  Since most slides are available online, I have only a few brief highlights below.  Next year’s KMWorld will be November 5-7, 2019.

The Think Creatively & Make Better Decisions keynote contained various interesting facts about the things that distract us and make us unproductive.  Distracted driving causes more deaths than drunk driving.  Attention spans have dropped from 12 seconds to 8 seconds (goldfish have a 9-second attention span).  Japan has texting lanes for walking.  71% of business meetings are unproductive, and 33% of employee time is spent in meetings. 281 billion emails were sent in 2018.  Don’t leave ideas and creative thinking to the few.  Mistakes shouldn’t be reprimanded.  Break down silos between departments.

The Deep Text Look at Text Analytics keynote explained that text mining is only part of text analytics.  Text mining treats words as things, whereas text analytics cares about meaning.  Sentiment analysis is now learning to handle things like: “I would have loved your product except it gave me a headache.”  It is hard for humans to pick good training documents for automatic categorization systems (what the e-discovery world calls predictive coding or technology-assisted review).  Computer-generated taxonomies are incredibly bad.  Deep learning is not like what humans do.  Deep learning takes 100,000 examples to detect a pattern, whereas humans will generalize (perhaps wrongly) from 2 examples.

The Cognitive Computing keynote mentioned that sarcasm makes sentiment analysis difficult.  For example: “I’m happy to spend a half hour of my lunch time in line at your bank.”  There are products to measure tone from audio and video.

The Don’t Stop at Stopwords: Function Words in Text Analytics session noted that function words, unlike content words, are added by the writer subconsciously.  Use of words like “that” or “the” instead of “this” can indicate the author is distancing himself/herself from the thing being described, possibly indicating deception.  They’ve used their techniques in about 20 different languages.  They need at least 300 words to make use of function word frequency to build a baseline.

The Should We Consign All Taxonomies to the Dustbin? talk considered the possibility of using machine learning to go directly from problem to solution without having a taxonomy in between.  He said that 100k documents or 1 million words of text are needed to get going.