Ruling limits use of statistical reasoning in legal arguments

A UK judge in murder trial ruled that Bayes' Theorem and similar statistical analyses can no longer be used to argue for the soundness of evidence in such trials. The Guardian has the story, A formula for justice.

"It's not often that the quiet world of mathematics is rocked by a murder case. But last summer saw a trial that sent academics into a tailspin, and has since swollen into a fevered clash between science and the law.
    At its heart, this is a story about chance. And it begins with a convicted killer, "T", who took his case to the court of appeal in 2010. Among the evidence against him was a shoeprint from a pair of Nike trainers, which seemed to match a pair found at his home. While appeals often unmask shaky evidence, this was different. This time, a mathematical formula was thrown out of court. The footwear expert made what the judge believed were poor calculations about the likelihood of the match, compounded by a bad explanation of how he reached his opinion. The conviction was quashed.
    But more importantly, as far as mathematicians are concerned, the judge also ruled against using similar statistical analysis in the courts in future. It's not the first time that judges have shown hostility to using formulae. But the real worry, say forensic experts, is that the ruling could lead to miscarriages of justice."

A group of UK academics, statisticians, forensic scientists and lawyers have been formed to try to address the problem bad statistical arguments in the courts. "We want to do what people failed to do in the past, which is really get the legal profession and statisticians and probability guys understanding each other's language."

Bayesian networks have become widely used in computing and information systems to model and reason about uncertainty and as prt of the foundation for artificial intelligence, machine learning and data mining.

Ruling limits use of statistical reasoning in legal arguments

A UK judge in murder trial ruled that Bayes' Theorem and similar statistical analyses can no longer be used to argue for the soundness of evidence in such trials. The Guardian has the story, A formula for justice.

"It's not often that the quiet world of mathematics is rocked by a murder case. But last summer saw a trial that sent academics into a tailspin, and has since swollen into a fevered clash between science and the law.
    At its heart, this is a story about chance. And it begins with a convicted killer, "T", who took his case to the court of appeal in 2010. Among the evidence against him was a shoeprint from a pair of Nike trainers, which seemed to match a pair found at his home. While appeals often unmask shaky evidence, this was different. This time, a mathematical formula was thrown out of court. The footwear expert made what the judge believed were poor calculations about the likelihood of the match, compounded by a bad explanation of how he reached his opinion. The conviction was quashed.
    But more importantly, as far as mathematicians are concerned, the judge also ruled against using similar statistical analysis in the courts in future. It's not the first time that judges have shown hostility to using formulae. But the real worry, say forensic experts, is that the ruling could lead to miscarriages of justice."

A group of UK academics, statisticians, forensic scientists and lawyers have been formed to try to address the problem bad statistical arguments in the courts. "We want to do what people failed to do in the past, which is really get the legal profession and statisticians and probability guys understanding each other's language."

Bayesian networks have become widely used in computing and information systems to model and reason about uncertainty and as prt of the foundation for artificial intelligence, machine learning and data mining.

Dear AASHE 2011 attendees,

Help us spread the great ideas shared at AASHE 2011 around the world in near real-time by participating in the AASHE 2011 Transcription Project. It's an ambitious project, but if even a small group of people commit themselves to helping out, we can rapidly expand the impact of AASHE 2011 far beyond Pittsburgh!

Why a Transcription Project?

Because it is a tried and true way to share and preserve the great ideas being presented at an AASHE conference! Last year, a couple AASHE staff typed what was being said in the sessions they attended and posted their transcripts on AASHE's blog. Within hours each transcript was getting hundreds of reads! People were tweeting them and facebooking them too.

Last year, it was exciting knowing that we were making it possible for ideas to reach people who couldn't attend in person! Even people who were in attendance appreciated the transcripts because they allowed them to find out what happened in a concurrent session they missed. And a year later, visitors to AASHE's website are still reading those transcripts!

What does it mean to transcribe a session?

Transcribing a session simply means you type what you hear as you hear it, as best as you can. It won't be word-for-word, but it should be faithful to the meaning intended by the presenter and use the presenter's terminology. In other words, we aren't asking for you to listen to a session, analyze it and write commentary about it afterwards.

How do I participate?

It's really easy!

1. Sit at the table with the "Transcription Project" sign at the front of any session room.
• Sitting at this table isn’t a requirement of participating, but in cases where multiple people arrive ready to transcribe a session it allows them to coordinate who will take and submit the transcription for that session.

2. In a blank email message or Word document, type what the presenters say!
• It does NOT have to be word for word! Try to capture their ideas faithfully, using their own terminology.
• When speakers change, indicate who the new speaker is by preceding their words with their name. For example, “John Doe: Our data shows that volunteers are awesome!“
• Here's an example transcription.

3. When the session is over, prepare your transcription for posting.
• Type the session name and the name of the presenters and their affiliation at top of the transcription.
• Include hyperlinks to any online information mentioned in the presentation, if you can.

4. Email your transcription to: transcriptions@aashe.org
• We’ll try to get it posted on the AASHE blog within 1 hour (www.aashe.org/blog)
• Tweet it and Facebook it when it goes live!

Does participating cut into the time I have to attend sessions?

No. In our experience, transcribing doesn't take away any time from attending sessions. During the session you write what you hear. After the session, perhaps while you are waiting for the next session to start, you check it over quickly and send it via email to transcriptions@aashe.org.

What if I'm not the fastest typer in the world? I'm afraid I won't do a good enough job.

Don't sweat it! You can summarize key ideas, like in this transcript from last year. Remember, a brief transcription is better than no transcription at all!

I don't think I can transcribe, but I want to help out with this great project. How else can I help?

Help us recruit more transcribers! Tweet or Facebook this page with the #aashe2011 tag. Encourage someone to transcribe a session if it looks like it's going to start without anyone transcribing it.

Help us spread the word when new transcriptions are posted by retweeting our announcements. We'll be tweeting with the #aashe2011 tag.

What's my reward for participating in the Transcription Project?

Besides the warm feeling that comes from knowing you helped share and preserve the great ideas presented at AASHE 2011, you'll get a shout-out when we post your transcription and tweet it out. You will also be entitled to a free AASHE volunteer t-shirt. Make sure you let us know your name when you send in your transcription.

Do you need transcribers for the keynotes and workshops?"

YES! If you would like to transcribe a keynote or workshop, please do! You can tweet out that you intend to transcribe one of these sessions so others know it is covered, or you can check with us at transcriptions@aashe.org

This year, October 7 has been designated as Ada Lovelace Day, a day to honor Augusta Ada King, Countess of Lovelace. She is widely regarded as the first computer programmer for her work with Charles Babbage who developed an early mechanical general-purpose computer, the analytical engine.

ReadWriteWeb commemorated the day with a story on the Women Who Made Google Plus and profiled twenty-two of the  Googlers:

"Launched on June 28th and just opened to the public at large late last month, Google Plus is a feature-rich social network with variable privacy and sharing settings at the core of its experience. Who were the women involved in building such a big, important technology? We asked, on Google Plus, and were told about twenty two of them profiled below. They are an incredibly accomplished group of people and a great source of inspiration for young women interested in science and technology – or for anyone else who could use some powerful role models. You should share this list of women with the girls in your life, though, that's what Ada Lovelace Day is all about."

You can visit their public G+ profiles and see more about who they are and what they do. The article points out that the 22 profiled are just some of the women who helped to build and launch this new and successful application.  

Many former UMBC students work at Google or have done internships there.  If you are interested in either, you can explore the opportunities at Google's pages for internships and jobs.