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Data ethics,
explainability,
interpretability,
and all that stuff
Joel Grus
Research Engineer, AI2
@joelgrus
ML Engineering
best practices
and ethics
Joel Grus
Research Engineer, AI2
@joelgrus
Caveat:
I work at AI2, and we are at AI2,
but I am speaking only on behalf
of me
● research engineer @AI2, I build deep
learning tools for NLP researchers
● previously SWE @Google, data science
@VoloMetrix + @Farecast + others
● small-time (but value-add) angel investor
(talk to me about your ML startups!)
● wrote a book about data science (2nd
edition now available)
● co-host of Adversarial Learning podcast
● "I Don't Like Notebooks"
● "Fizz Buzz in Tensorflow"
about me
data ethics
● explicitly political HCI
● anarcho-communism
● prefigurative counterpower
● REVO-LUTION
● decontextualization
● technological solutionism
DATA
ETHICS!
my pet causes that have nothing to do with data or ethics
● unschooling
●
interpretability
explainability
best
practices
(if you want
to understand
your models)
(but also if
you don't)
make sure your models are doing what you think
they're doing
code review YOUR ML CODE
write unit tests
tiny known dataset
check that model runs
check that output has the right fields
check that output has the right shape
check that output has reasonable values
make it easy to make your models
do something different
separatE library code and experiment code
Make It Easy To Vary Parameters
export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12
export GLUE_DIR=/path/to/glue
python run_classifier.py 
--task_name=MRPC 
--do_train=true 
--do_eval=true 
--data_dir=$GLUE_DIR/MRPC 
--vocab_file=$BERT_BASE_DIR/vocab.txt 
--bert_config_file=$BERT_BASE_DIR/bert_config.json 
--init_checkpoint=$BERT_BASE_DIR/bert_model.ckpt 
--max_seq_length=128 
--train_batch_size=32 
--learning_rate=2e-5 
--num_train_epochs=3.0 
--output_dir=/tmp/mrpc_output/
ablation
mini-case-study:
allennlp
what is allennlp?
programming to higher level abstractions
# models/crf_tagger.py
class CrfTagger(Model):
"""
The ``CrfTagger`` encodes a sequence of text with a ``Seq2SeqEncoder``,
then uses a Conditional Random Field model to predict a tag for each token in the sequence.
def __init__(self, vocab: Vocabulary,
text_field_embedder: TextFieldEmbedder,
encoder: Seq2SeqEncoder,
label_namespace: str = "labels",
feedforward: Optional[FeedForward] = None,
label_encoding: Optional[str] = None,
include_start_end_transitions: bool = True,
constrain_crf_decoding: bool = None,
calculate_span_f1: bool = None,
dropout: Optional[float] = None,
verbose_metrics: bool = False,
initializer: InitializerApplicator = InitializerApplicator(),
regularizer: Optional[RegularizerApplicator] = None) -> None:
super().__init__(vocab, regularizer)
...
declarative configuration
"model": {
"type": "crf_tagger",
"label_encoding": "BIOUL",
"dropout": 0.5,
"include_start_end_transitions": false,
"text_field_embedder": {
"token_embedders": {
"tokens": {
"type": "embedding",
"embedding_dim": 50,
"pretrained_file": "/path/to/glove.txt.gz",
"trainable": true
},
"elmo":{
"type": "elmo_token_embedder",
"options_file": "/path/to/elmo/options.json",
"weight_file": "/path/to/elmo/weights.hdf5",
"do_layer_norm": false,
"dropout": 0.0
},
"token_characters": {
"type": "character_encoding",
"embedding": {
"embedding_dim": 16
},
"encoder": {
"type": "cnn",
"embedding_dim": 16,
"num_filters": 128,
"ngram_filter_sizes": [3],
"conv_layer_activation": "relu"
}
}
}
},
"encoder": {
"type": "lstm",
"input_size": 1202,
"hidden_size": 200,
"num_layers": 2,
"dropout": 0.5,
"bidirectional": true
},
"regularizer": [
[
"scalar_parameters",
{
"type": "l2",
"alpha": 0.1
}
]
]
},
"What would
BERT vectors add
to my model
compared with
glove vectors?"
"Oh, great, now
I'm going to make
lots of changes to
my code and
maintain all these
different versions
so that my results
are reproducible"
"I'll just make a
new config file for
the BERT version!"
"token_indexers": {
"tokens": {
"type": "single_id",
"lowercase_tokens": true
},
"token_characters": {
"type": "characters",
"min_padding_length": 3
}
}
"token_indexers": {
"bert": {
"type": "bert-pretrained",
"pretrained_model": std.extVar("BERT_VOCAB"),
"do_lowercase": false,
"use_starting_offsets": true
},
"token_characters": {
"type": "characters",
"min_padding_length": 3
}
}
"text_field_embedder": {
"token_embedders": {
"tokens": {
"type": "embedding",
"embedding_dim": 50,
"pretrained_file": "/path/to/glove.tar.gz",
"trainable": true
},
"token_characters": {
"type": "character_encoding",
"embedding": {
"embedding_dim": 16
},
"encoder": {
"type": "cnn",
"embedding_dim": 16,
"num_filters": 128,
"ngram_filter_sizes": [3],
"conv_layer_activation": "relu"
}
}
},
},
"text_field_embedder": {
"allow_unmatched_keys": true,
"embedder_to_indexer_map": {
"bert": ["bert", "bert-offsets"],
"token_characters": ["token_characters"],
},
"token_embedders": {
"bert": {
"type": "bert-pretrained",
"pretrained_model": std.extVar("BERT_WEIGHTS")
},
"token_characters": {
"type": "character_encoding",
"embedding": {
"embedding_dim": 16
},
"encoder": {
"type": "cnn",
"embedding_dim": 16,
"num_filters": 128,
"ngram_filter_sizes": [3],
"conv_layer_activation": "relu"
}
}
}
},
THanks!
Any questions?
● me: @joelgrus
● AllenNLP: allennlp.org
● podcast: adversariallearning.com

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Rsqrd AI: ML Engineering Best Practices and Ethics

  • 1. Data ethics, explainability, interpretability, and all that stuff Joel Grus Research Engineer, AI2 @joelgrus
  • 2. ML Engineering best practices and ethics Joel Grus Research Engineer, AI2 @joelgrus
  • 3. Caveat: I work at AI2, and we are at AI2, but I am speaking only on behalf of me
  • 4.
  • 5.
  • 6. ● research engineer @AI2, I build deep learning tools for NLP researchers ● previously SWE @Google, data science @VoloMetrix + @Farecast + others ● small-time (but value-add) angel investor (talk to me about your ML startups!) ● wrote a book about data science (2nd edition now available) ● co-host of Adversarial Learning podcast ● "I Don't Like Notebooks" ● "Fizz Buzz in Tensorflow" about me
  • 8.
  • 9.
  • 10. ● explicitly political HCI ● anarcho-communism ● prefigurative counterpower
  • 11. ● REVO-LUTION ● decontextualization ● technological solutionism
  • 13. my pet causes that have nothing to do with data or ethics ● unschooling ●
  • 15.
  • 17.
  • 18. best practices (if you want to understand your models) (but also if you don't)
  • 19. make sure your models are doing what you think they're doing
  • 20. code review YOUR ML CODE
  • 21. write unit tests tiny known dataset check that model runs check that output has the right fields check that output has the right shape check that output has reasonable values
  • 22. make it easy to make your models do something different
  • 23. separatE library code and experiment code
  • 24. Make It Easy To Vary Parameters export BERT_BASE_DIR=/path/to/bert/uncased_L-12_H-768_A-12 export GLUE_DIR=/path/to/glue python run_classifier.py --task_name=MRPC --do_train=true --do_eval=true --data_dir=$GLUE_DIR/MRPC --vocab_file=$BERT_BASE_DIR/vocab.txt --bert_config_file=$BERT_BASE_DIR/bert_config.json --init_checkpoint=$BERT_BASE_DIR/bert_model.ckpt --max_seq_length=128 --train_batch_size=32 --learning_rate=2e-5 --num_train_epochs=3.0 --output_dir=/tmp/mrpc_output/
  • 28. programming to higher level abstractions # models/crf_tagger.py class CrfTagger(Model): """ The ``CrfTagger`` encodes a sequence of text with a ``Seq2SeqEncoder``, then uses a Conditional Random Field model to predict a tag for each token in the sequence. def __init__(self, vocab: Vocabulary, text_field_embedder: TextFieldEmbedder, encoder: Seq2SeqEncoder, label_namespace: str = "labels", feedforward: Optional[FeedForward] = None, label_encoding: Optional[str] = None, include_start_end_transitions: bool = True, constrain_crf_decoding: bool = None, calculate_span_f1: bool = None, dropout: Optional[float] = None, verbose_metrics: bool = False, initializer: InitializerApplicator = InitializerApplicator(), regularizer: Optional[RegularizerApplicator] = None) -> None: super().__init__(vocab, regularizer) ...
  • 29. declarative configuration "model": { "type": "crf_tagger", "label_encoding": "BIOUL", "dropout": 0.5, "include_start_end_transitions": false, "text_field_embedder": { "token_embedders": { "tokens": { "type": "embedding", "embedding_dim": 50, "pretrained_file": "/path/to/glove.txt.gz", "trainable": true }, "elmo":{ "type": "elmo_token_embedder", "options_file": "/path/to/elmo/options.json", "weight_file": "/path/to/elmo/weights.hdf5", "do_layer_norm": false, "dropout": 0.0 }, "token_characters": { "type": "character_encoding", "embedding": { "embedding_dim": 16 }, "encoder": { "type": "cnn", "embedding_dim": 16, "num_filters": 128, "ngram_filter_sizes": [3], "conv_layer_activation": "relu" } } } }, "encoder": { "type": "lstm", "input_size": 1202, "hidden_size": 200, "num_layers": 2, "dropout": 0.5, "bidirectional": true }, "regularizer": [ [ "scalar_parameters", { "type": "l2", "alpha": 0.1 } ] ] },
  • 30. "What would BERT vectors add to my model compared with glove vectors?"
  • 31. "Oh, great, now I'm going to make lots of changes to my code and maintain all these different versions so that my results are reproducible" "I'll just make a new config file for the BERT version!"
  • 32. "token_indexers": { "tokens": { "type": "single_id", "lowercase_tokens": true }, "token_characters": { "type": "characters", "min_padding_length": 3 } } "token_indexers": { "bert": { "type": "bert-pretrained", "pretrained_model": std.extVar("BERT_VOCAB"), "do_lowercase": false, "use_starting_offsets": true }, "token_characters": { "type": "characters", "min_padding_length": 3 } }
  • 33. "text_field_embedder": { "token_embedders": { "tokens": { "type": "embedding", "embedding_dim": 50, "pretrained_file": "/path/to/glove.tar.gz", "trainable": true }, "token_characters": { "type": "character_encoding", "embedding": { "embedding_dim": 16 }, "encoder": { "type": "cnn", "embedding_dim": 16, "num_filters": 128, "ngram_filter_sizes": [3], "conv_layer_activation": "relu" } } }, }, "text_field_embedder": { "allow_unmatched_keys": true, "embedder_to_indexer_map": { "bert": ["bert", "bert-offsets"], "token_characters": ["token_characters"], }, "token_embedders": { "bert": { "type": "bert-pretrained", "pretrained_model": std.extVar("BERT_WEIGHTS") }, "token_characters": { "type": "character_encoding", "embedding": { "embedding_dim": 16 }, "encoder": { "type": "cnn", "embedding_dim": 16, "num_filters": 128, "ngram_filter_sizes": [3], "conv_layer_activation": "relu" } } } },
  • 34. THanks! Any questions? ● me: @joelgrus ● AllenNLP: allennlp.org ● podcast: adversariallearning.com