A Theory: No Room for Shortcuts in Healthcare Datasets
July 1, 2021
The value of any machine learning algorithm depends on the data it was trained on, we are reminded in the article, “Machine Learning Deserves Better Than This” at AAAS’ Science Mag. Writer Derek Lowe makes some good points that are, nevertheless, likely to make him unpopular among the rah-rah AI crowd. He is specifically concerned with the ways machine learning is currently being applied in healthcare. As an example, Lowe examines a paper on coronavirus pathology as revealed in lung X-ray data. He writes:
“Every single one of the studies falls into clear methodological errors that invalidate their conclusions. These range from failures to reveal key details about the training and experimental data sets, to not performing robustness or sensitivity analyses of their models, not performing any external validation work, not showing any confidence intervals around the final results (or not revealing the statistical methods used to compute any such), and many more. A very common problem was the (unacknowledged) risk of bias right up front. Many of these papers relied on public collections of radiological data, but these have not been checked to see if the scans marked as COVID-19 positive patients really were (or if the ones marked negative were as well). It also needs to be noted that many of these collections are very light on actual COVID scans compared to the whole database, which is not a good foundation to work from, either, even if everything actually is labeled correctly by some miracle. Some papers used the entire dataset in such cases, while others excluded images using criteria that were not revealed, which is naturally a further source of unexamined bias.”
As our regular readers are aware, any AI is only as good as the data it is trained upon. However, data scientists can be so eager to develop tools (or, to be less charitable, to get published) that they take shortcuts. Some, for example, accept all data from public databases without any verification. Others misapply data, like the collection of lung x-rays from patients under the age of five that was included in the all-ages pneumonia dataset. Then there are the datasets and algorithms that simply do not have enough documentation to be trusted. How was the imaging data pre-processed? How was the model trained? How was it selected and validated? Crickets.
We understand why people are excited about the potential of machine learning in healthcare, a high-stakes field where solutions can be frustratingly elusive. However, it benefits no one to rely on conclusions drawn from flawed data. In fact, doing so can be downright dangerous. Let us take the time to get machine learning right first.
Cynthia Murrell, July 1, 2021
TikTok: What Is the Problem? None to Sillycon Valley Pundits.
June 18, 2021
I remember making a comment in a DarkCyber video about the lack of risk TikTok posed to its users. I think I heard a couple of Sillycon Valley pundits suggest that TikTok is no big deal. Chinese links? Hey, so what. These are short videos. Harmless.
Individuals like this are lost in clouds of unknowing with a dusting of gold and silver naive sparkles.
“TikTok Has Started Collecting Your ‘Faceprints’ and ‘Voiceprints.’ Here’s What It Could Do With Them” provides some color for parents whose children are probably tracked, mapped, and imaged:
Recently, TikTok made a change to its U.S. privacy policy,allowing the company to “automatically” collect new types of biometric data, including what it describes as “faceprints” and “voiceprints.” TikTok’s unclear intent, the permanence of the biometric data and potential future uses for it have caused concern
Well, gee whiz. The write up is pretty good, but there are a couple of uses of these types of data left out of the write up:
- Cross correlate the images with other data about a minor, young adult, college student, or aging lurker
- Feed the data into analytic systems so that predictions can be made about the “flexibility” of certain individuals
- Cluster young people into egg cartons so fellow travelers and their weakness could be exploited for nefarious or really good purposes.
Will the Sillycon Valley real journalists get the message? Maybe if I convert this to a TikTok video.
Stephen E Arnold, June 18, 2021
Google Encourages Competition: Our Way or No Way. Seems Fair
June 4, 2021
I get a kick out of the Google. First, there was the really embarrassing matter of the diversity director outputting a few years ago some spicy comments about a country. Here’s a rundown of what makes the Timnit Gebru affair like just another synthetic pearl in a long string of management jewelry at a flea market.
I found this story even more revealing. The context is that numerous legal eagles are slapping Googzilla with a wide range of legal documents. Many of these are related to alleged monopolistic practices. I am no lawyer, but I get the feeling that some people are concerned about Google’s ability to absorb online advertising revenues, control what information people can find via the universal search thing, and Google’s Amazon like arrogance. (Yep, Amazon is the new Big Dog, but you knew that, right?)
Here’s the key statement:
Today I Learned you can not advertise on @GoogleAds if you use @googleanalytics competitors like @matomo_org
This seems reasonable. An “if then” statement for organizations that want to tap into Google’s billions of “users.”
An entity called @HashNuke added:
This is easily identifiable as anti-competitive practice. Wouldn’t this be illegal in many countries?
If these statements are accurate, isn’t being Googley just the best way to inspire individuals and organizations. Some of those legal eagles may find the information worth checking out.
Stephen E Arnold, June 4, 2021
Data Silos vs. Knowledge Graphs
May 26, 2021
Data scientist and blogger Dan McCreary has high hopes for his field’s future, describing what he sees as the upcoming shift “From Data Science to Knowledge Science.” He predicts:
“I believe that within five years there will be dramatic growth in a new field called Knowledge Science. Knowledge scientists will be ten times more productive than today’s data scientists because they will be able to make a new set of assumptions about the inputs to their models and they will be able to quickly store their insights in a knowledge graph for others to use. Knowledge scientists will be able to assume their input features:
- Have higher quality
- Are harmonized for consistency
- Are normalized to be within well-defined ranges
- Remain highly connected to other relevant data as such as provenance and lineage metadata”
Why will this evolution occur? Because professionals are motivated to develop their way past the current tedious state of affairs—we are told data scientists typically spend 50% to 80% of their time on data clean-up. This leaves little time to explore the nuggets of knowledge they eventually find among the weeds.
As McCreary sees it, however, the keys to a solution already exist. For example, machine learning can be used to feed high-quality, normalized data into accessible and evolving knowledge graphs. He describes how MarkLogic, where he used to work, developed and uses data quality scores. Such scores would be key to building knowledge graphs that analysts can trust. See the post for more details on how today’s tedious data science might evolve into this more efficient “knowledge science.” We hope his predictions are correct, but only time will tell. About five years, apparently.
Cynthia Murrell, May 26, 2021
Reengineering Bias: What an Interesting Idea
April 5, 2021
If this is true, AI may be in trouble now. VentureBeat reports, “Researchers Find that Debiasing Doesn’t Eliminate Racism from Hate Speech Detection Models.” It is known that AI systems meant to detect toxic language themselves have a problem with bias. Specifically, they tend to flag text by Black users more often than text by white users. Oh, the irony. The AI gets hung up on language markers often found in vernaculars like African-American English (AAE). See the article for a few examples. Researchers at the Allen Institute tried several techniques to reteach existing systems to be more even handed. Reporter Kyle Wiggers writes:
“In the course of their work, the researchers looked at one debiasing method designed to tackle ‘predefined biases’ (e.g., lexical and dialectal). They also explored a process that filters ‘easy’ training examples with correlations that might mislead a hate speech detection model. According to the researchers, both approaches face challenges in mitigating biases from a model trained on a biased dataset for toxic language detection. In their experiments, while filtering reduced bias in the data, models trained on filtered datasets still picked up lexical and dialectal biases. Even ‘debiased’ models disproportionately flagged text in certain snippets as toxic. Perhaps more discouragingly, mitigating dialectal bias didn’t appear to change a model’s propensity to label text by Black authors as more toxic than white authors. In the interest of thoroughness, the researchers embarked on a proof-of-concept study involving relabeling examples of supposedly toxic text whose translations from AAE to ‘white-aligned English’ were deemed nontoxic. They used OpenAI’s GPT-3 to perform the translations and create a synthetic dataset — a dataset, they say, that resulted in a model less prone to dialectal and racial biases.”
The researchers acknowledge that re-writing Black users’ posts to sound more white is not a viable solution. The real fix would be to expose AI systems to a wider variety of dialects in the original training phase, but will developers take the trouble? Like many people, once hate-speech detection bots become prejudiced it is nigh impossible to train them out of it.
Cynthia Murrell, April 5, 2021
Google and Cookies: Crafting Quite Tasty Bait
March 19, 2021
I read “Alphabet: Five Things We Know about Google’s Ad Changes after Cookies.” I approached the write up with some interest. Cookies have been around for a long time. The reason? They allowed a number of interesting functions, including tracking, cross correlation of user actions, and a covert existence.
Now, no more Google cookies.
The write up explains what Google wants keen observers, real journalists, and thumbtypers to know; to wit:
- Privacy is really, really important to Google—now. Therefore, the GOOG won’t support third party cookies. Oh, shucks, what about cross site tracking? Yeah, what about it?
- Individuals can be targeted. Those with a rifle shot orientation have to provide data to the Google and use the Google software system called “customer match.” Yeah, ad narrowcasting lives.
- Google will draw some boundaries about its data leveraging for advertisers. But what about “publishers”? Hey, Google has some special rules. Yeah, a permeable membrane for certain folks.
- FLOC makes non-personalized ad targeting possible. I want to write, “You’ve been FLOC’ed” but I shall not. Yeah, FLOC. But you can always try FLEDGE. So “You’ve been FLEDGED” is a possibility.
How’s this work? The write up does not shed any light. Here’s a question for a “real news” outfit to tackle:
How many data points does a disambiguation system require to identify a name, location, and other personal details of a single individual?
Give up. Better not. Oh, the bait, pivoted cookies. Great for catching prospects I think.
Stephen E Arnold, March 19, 2021
Watching the Future of Talend
March 15, 2021
I read “Talend Sells to Private Equity Firm Thoma Bravo in $2.4 Billion Deal.” I find this interesting. Talend is a software company providing extract, transform, and load services and analytics. Data remain the problem for many thumbtypers fresh from Amazon or Google certification classes. The idea is to suck in legally data from different sources. These data are often in odd ball formats to malformed because another digital mechanic missed a bolt or added a bit of finery. Some people love MarkLogic innovations in XML; others, not so enamored of the tweaks.
What’s Thoma Bravo bring to the table for a publicly traded company with a number of competitors?
I can think of two benefits:
The first is MBA think. Thoma Bravo is skilled in the methods for making a company more efficient. It is a good idea to internalize the definition of “efficiency” as the word is used at McKinsey & Co.
The second is acquisition think. From my point of view, the idea is to identify interesting companies which provide additional functionality around the core Talend business. Then Thoma Bravo assists the Talend management to bring these companies into the mothership, train sales professionals, and close deals.
No problem exists with this game plan. One can identify some indicators to monitor; for example:
- Executive turnover
- Realigning expenditures; possibly taking money from security and allocating the funds to sales and marketing
- Targeting specific market segments with special bundles of enhanced Talend software and business methods.
For more information about Talend as it exists in March 2021, navigate to this link.
Oh, one final comment. Thoma Bravo was involved in making SolarWinds the business success it became.
Stephen E Arnold, March 15, 2021
About TikTok and Privacy: $92 Million Catch Your Attention
March 4, 2021
I have commented about the superficial understanding shared among some “real” and big time journalists of data collection. What’s the big deal about TikTok? Who cares what kids are doing? Dismissive attitude flipped off these questions because “real” news knows what’s up?
“ByteDance Agrees to US$92 Million Privacy Settlement with US TikTok Users” suggests that ignoring the China-linked TikTok may warrant some scrutiny. The story reports:
The lawsuits claimed the TikTok app “infiltrates its users’ devices and extracts a broad array of private data including biometric data and content that defendants use to track and profile TikTok users for the purpose of, among other things, ad targeting and profit.” The settlement was reached after “an expert-led inside look at TikTok’s source code” and extensive mediation efforts, according to the motion seeking approval of the settlement.
My view is that tracking a user via a range of methods can create a digital fingerprint of a TikTok user. That fingerprint can be matched or cross correlated with other data available to a specialist; for example, information obtained from Oracle. The result is that a user could be identified and tracked across time.
Yep, today’s young person is tomorrow’s thumbtyper in one of the outfits compromised by the SolarWinds’ misstep. What if the TikTok data make it possible to put pressure on a user? What if the user releases access information or other high value data?
TikTok, TikTok, the clock may be ticketing quietly away.
Stephen E Arnold, March 4, 2021
Business Intelligence, Expectations, and Data Fog
February 10, 2021
Business intelligence and government intelligence software promises real time data intake, analysis and sense making, and outputs with a mouse click. Have you heard the phrase, “I have the Brooklyn Bridge for sale”? Sure, sure, I know I don’t want to own the Brooklyn Bridge, but that super spiffy intelligence software (what I call intelware), count me in.
The marketing pitch for business intelligence and general intelligence software has not changed significantly over the years. In my experience, a couple of nifty outputs like a relationship diagram and a series of buttons set up to spit out “actionable intelligence” often close the deal. The users of the software usually discover three points not making up a large part of the demos, the discussions, and the final contract for the customer’s requirements.
I read “The Age Of Continuous Business Intelligence.” The idea is appealing. Lots of information and no time to read, review, digest, analyze, and discuss the available information. In my opinion, the attitude now is “I don’t have time.”
Yep, time.
The write up asserts:
we [an outfit called KX] know that shortening the time it takes to ingest, store, process, and analyze historic and real-time data is a game changer for businesses in all sectors. Our customers in finance, manufacturing, automotive, telecommunications and utilities tell us that when processes and systems are continuously fed by real-time data that is enriched by the context of historic data, they can automate critical business decisions resulting in significant operational and commercial benefits.
The write up contains a diagram which lays bare “continuous business intelligence.”
The write up concludes:
As the research clearly shows, real-time data analytics is a critical area of investment for many firms. To ensure maximum value is derived from these investments, it is imperative that organizations – regardless of size and sector – challenge their understanding of what real-time means. By implementing a strategy of continuous business intelligence, firms can dramatically reduce the time it takes to uncover and act on insights that can materially change the game in terms of growth, efficiency and profitability.
I love that “research clearly shows.” The challenges for the continuous thing include:
- Defining real time. (According to research my team did for a project years ago, there are numerous definitions of real time, and there is a Grand Canyon sized gap among these.)
- Making clear the computational short cuts necessary to process “fire hoses”. (Yep, these compromises have a significant impact on costs, validity of system outputs, and the mechanisms for issuing meaningful outputs from sense making.)
- Managing the costs. (Normalizing, verifying, processing, storing, and moving data require human and machine resources. Right, those things.)
Net net: Software whether for business or government applications in intelligence work only if the focus is narrow and the expectations of a wild and crazy MBA are kept within a reality corral. Otherwise, business intelligence will shoot blanks, not silver bullets.
Oh, KX is hooked up with a mid tier consulting firm. What’s that mean? A sudden fog has rolled in, and it is an expensive fog.
Stephen E Arnold, February 10, 2021
Dashboards Evil? Worth a Thought. Nah, Just Take What Is Output
February 1, 2021
Business intelligence (BI) is jargon for the technologies and strategies used to manage business data analytics. It is a fancy term for standard operating procedures and looks good on a resume, but one IT CEO wants to make it obsolete. Diginomica discusses how BI could head to the recycling bin in the article: “ThoughtSpot CEO-‘I Want To Kill BI And I Want All Dashboards To Die.’”
COVID-19 has changed global business practices and technology experts spent 2020 investigating ways to aggregate business data. ThoughtSpot CEO Sudheesh Nair explained in the article that traditional aggregation patterns do not apply anymore and companies need to change in order to maintain their customers. Nair believes his ThoughtSpot platform, described as ‘Google for numbers,’ will deliver key insights the same way Google provides information.
Nair pointed out that opinions are easily accessible via a Google search, but facts are harder to find in the endless search results. Nair wants his ThoughSpot platform to make facts as easily accessible as opinions. ThoughtSpot combines hardened facts with a NLP interface to make finding facts easier, think Windows 95 versus the old command land interface:
“ThoughtSpot does this by allowing users to search enterprise data with hyper-personalized questions using natural language processing. It aims to not only give a result for the question you ask, but then also uses AI to offer up alternative questions and results that may be helpful. This is very different to traditional BI, which typically offers you a template for which to present historical, aggregate data.”
In other words, Nair wants to sift the information noise from facts. Today’s BI dashboards offer a plethora of information, but lack personalization notes that could win new customers and retain older ones.
ThoughtSpot will supposedly combine old data with new data to push out BI dashboards and create a new data analytics space for businesses. Nair’s description of ThoughtSpot is an interesting pitch, but it sounds more like a new way to search information. Instead of explaining how ThoughtSpot works it would be better to offer demonstrations of its capabilities.
But thinking? Not the core competency of the thumb typing generations.
Whitney Grace, February 1, 2021
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