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The problem was there before, but there are reasons why Machine Learning is amplifying bad practices. In the past people were manually fishing for results in a

• barry-cotter 8y ago
  Medicine may be better than ML but there’s not much in the difference. > COMPare: Qualitative analysis of researchers’ responses to critical correspondence on a cohort of 58 misreported trials > Background > Discrepancies between pre-specified and reported outcomes are an important and prevalent source of bias in clinical trials. COMPare (Centre for Evidence-Based Medicine Outcome Monitoring Project) monitored all trials in five leading journals for correct outcome reporting, submitted correction letters on all misreported trials in real time, and then monitored responses from editors and trialists. From the trialists’ responses, we aimed to answer two related questions. First, what can trialists’ responses to corrections on their own misreported trials tell us about trialists’ knowledge of correct outcome reporting? Second, what can a cohort of responses to a standardised correction letter tell us about how researchers respond to systematic critical post-publication peer review? > Results > Trialists frequently expressed views that contradicted the CONSORT (Consolidated Standards of Reporting Trials) guidelines or made inaccurate statements about correct outcome reporting. Common themes were: stating that pre-specification after trial commencement is acceptable; incorrect statements about registries; incorrect statements around the handling of multiple time points; and failure to recognise the need to report changes to pre-specified outcomes in the trial report. We identified additional themes in the approaches taken by researchers when responding to critical correspondence, including the following: ad hominem criticism; arguing that trialists should be trusted, rather than follow guidelines for trial reporting; appealing to the existence of a novel category of outcomes whose results need not necessarily be reported; incorrect statements by researchers about their own paper; and statements undermining transparency infrastructure, such as trial registers. https://trialsjournal.biomedcentral.com/articles/10.1186/s13063-019-3172-3 https://trialsjournal.biomedcentral.com/articles/10.1186/s13...
  • hannob 8y ago
    I am just in the process of digging into this paper and covering it in an article, so I'm quite familiar with it. But as bad as this is: What the COMPare project is doing here is documenting the flaws of a process to counter bad scientific practice. The reality in most fields (including pretty much all of CS and ML) is that no such process exists at all, because noone even tries to fix these issues. So you have medicine where people try to fix these issues (and are - admittedly - not very good at it) versus other fields that don't even try.
    • mistrial9 8y ago
      its quite a stretch to say that CS does not have reproducibility ! .. probably want to define that a bit
    • bjourne 8y ago
      This is definitely true. A common "blueprint" for articles in applied CS is first they propose a "novel" algorithm. This algorithm may be very similar to an existing algorithm and in many cases is identical to one. Then they benchmark the algorithm and shows that it performs better on some metrics than existing solutions. These benchmarks are often quite poor, and if you vary them a little, the purported performance increases vanishes.
    • Amygaz 8y ago
      Are you planning on touching on autoML? Do you think that could help?
• neltnerb 8y ago
  If we broaden Machine Learning to apply to data fitting tools, I can see how it would apply broadly and with no ill intent to produce errors. Consider the simple task of peak fitting to determine the result for some data. You're probably using a commercial tool to identify the peak position, calculate the baseline, and come up with parameters for your model. But if there's an error, and at least when I was in grad school the tools often would get stuck in weird local minima that take experience to recognize, it could easily just never be noticed. If your baseline is way off, good luck calculating your peak areas reproducibly... Data analysis is hard, and it's easy to trust algorithms to be at least more reproducible than doing it more manually. Plus side if you provide your dataset and code others can at least redo the analysis! Really excited to see more Jupyter notebooks used for publications in the future.
• evrydayhustling 8y ago
  Can you give a citation about ML being used this way and producing overtreatment? I'm aware of experimental results with e.g. Watson that were first overstated and then rejected, bit that seemed like a situation where an institution experimented with a poor use of ML and successfully rejected it, not where they falsely accepted an ML result.
• cle 8y ago
  It seems what ML is really doing is exposing weaknesses in our scientific processes. The appropriate response here is to fix the processes, instead of blaming the latest fad and imploring people to "try harder". If the root cause isn't fixed, the next fad after ML will cause the same thing again. What feasible systemic changes can we make so that scientists can't get away with publishing sloppy results? It's not an interesting question for many scientists, who prefer focusing on technical solutions over political ones.
  • Certhas 8y ago
    I think it is a very interesting question for a lot of scientists, but also an extremely hard one to answer. And an even harder one to implement. Even obvious wins that almost everyone can agree on, like getting publishing out of the hands of for-profit entities that add no value is taking forever, because cultural, social and political institutions are hard to move.